Oracle University Podcast

Curious about what really goes on inside a cloud data center?   In this episode, Lois Houston and Nikita Abraham chat with Principal OCI Instructor Orlando Gentil about how cloud data centers are transforming the way organizations manage technology.   They explore the differences between traditional and cloud data centers, the roles of CPUs, GPUs, and RAM, and why operating systems and remote access matter more than ever.   Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services.   Nikita: Hi everyone! Today, we’re covering the fundamentals you need to be successful in a cloud environment. If you’re new to cloud, coming from a SaaS environment, or planning to move from on-premises to the cloud, you won’t want to miss this. With us today is Orlando Gentil, Principal OCI Instructor at Oracle University. Hi Orlando! Thanks for joining us.   01:01 Lois: So Orlando, we know that Oracle has been a pioneer of cloud technologies and has been pivotal in shaping modern cloud data centers, which are different from traditional data centers. For our listeners who might be new to this, could you tell us what a traditional data center is?  Orlando: A traditional data center is a physical facility that houses an organization's mission critical IT infrastructure, including servers, storage systems, and networking equipment, all managed on site.   01:32 Nikita: So why would anyone want to use a cloud data center?  Orlando: The traditional model requires significant upfront investment in physical hardware, which you are then responsible for maintaining along with the underlying infrastructure like physical security, HVAC, backup power, and communication links.  In contrast, cloud data centers offer a more agile approach. You essentially rent the infrastructure you need, paying only for what you use. In the traditional data center, scaling resources up and down can be a slow and complex process.  On cloud data centers, scaling is automated and elastic, allowing resources to adjust dynamically based on demand. This shift allows business to move their focus from the constant upkeep of infrastructure to innovation and growth.  The move represents a shift from maintenance to momentum, enabling optimized costs and efficient scaling. This fundamental shift is how IT infrastructure is managed and consumed, and precisely what we mean by moving to the cloud.  02:39 Lois: So, when we talk about moving to the cloud, what does it really mean for businesses today?  Orlando: Moving to the cloud represents the strategic transition from managing your own on-premise hardware and software to leveraging internet-based computing services provided by a third-party.  This involves migrating your applications, data, and IT operations to a cloud environment. This transition typically aims to reduce operational overhead, increase flexibility, and enhance scalability, allowing organizations to focus more on their core business functions.    03:17 Nikita: Orlando, what’s the “brain” behind all this technology?  Orlando: A CPU or Central Processing Unit is the primary component that performs most of the processing inside the computer or server. It performs calculations handling the complex mathematics and logic that drive all applications and software.  It processes instructions, running tasks, and operations in the background that are essential for any application. A CPU is critical for performance, as it directly impacts the overall speed and efficiency of the data center.  It also manages system activities, coordinating user input, various application tasks, and the flow of data throughout the system. Ultimately, the CPU drives data center workloads from basic server operations to powering cutting edge AI applications.  04:10 Lois: To better understand how a CPU achieves these functions and processes information so efficiently, I think it’s important for us to grasp its fundamental architecture. Can you briefly explain the fundamental architecture of a CPU, Orlando?  Orlando: When discussing CPUs, you will often hear about sockets, cores, and threads. A socket refers to the physical connection on the motherboard where a CPU chip is installed.  A single server motherboard can have one or more sockets, each holding a CPU. A core is an independent processing unit within a CPU. Modern CPUs often have multiple cores, enabling them to handle several instructions simultaneously, thus increasing processing power.  Think of it as having multiple mini CPUs on a single chip. Threads are virtual components that allow a single CPU core to handle multiple sequence of instructions or threads concurrently. This technology, often called hyperthreading, makes a single core appear as two logical processors to the operating system, further enhancing efficiency.  05:27 Lois: Ok. And how do CPUs process commands?  Orlando: Beyond these internal components, CPUs are also designed based on different instruction set architectures which dictate how they process commands.   CPU architectures are primarily categorized in two designs-- Complex Instruction Set Computer or CISC and Reduced Instruction Set Computer or RISC. CISC processors are designed to execute complex instructions in a single step, which can reduce the number of instructions needed for a task, but often leads to a higher power consumption.  These are commonly found in traditional Intel and AMD CPUs. In contrast, RISC processors use a simpler, more streamlined set of instructions. While this might require more steps for a complex task, each step is faster and more energy efficient. This architecture is prevalent in ARM-based CPUs.  06:34 Are you looking to boost your expertise in enterprise AI? Check out the Oracle AI Agent Studio for Fusion Applications Developers course and professional certification—now available through Oracle University. This course helps you build, customize, and deploy AI Agents for Fusion HCM, SCM, and CX, with hands-on labs and real-world case studies. Ready to set yourself apart with in-demand skills and a professional credential? Learn more and get started today! Visit mylearn.oracle.com for more details.     07:09 Nikita: Welcome back! We were discussing CISC and RISC processors. So Orlando, where are they typically deployed? Are there any specific computing environments and use cases where they excel?  Orlando: On the CISC side, you will find them powering enterprise virtualization and server workloads, such as bare metal hypervisors in large databases where complex instructions can be efficiently processed. High performance computing that includes demanding simulations, intricate analysis, and many traditional machine learning systems.  Enterprise software suites and business applications like ERP, CRM, and other complex enterprise systems that benefit from fewer steps per instruction. Conversely, RISC architectures are often preferred for cloud-native workloads such as Kubernetes clusters, where simpler, faster instructions and energy efficiency are paramount for distributed computing.  Mobile device management and edge computing, including cell phones and IoT devices where power efficiency and compact design are critical. Cost optimized cloud hosting supporting distributed workloads where the cumulative energy savings and simpler design lead to more economical operations.  The choice between CISC and RISC depends heavily on the specific workload and performance requirements. While CPUs are versatile generalists, handling a broad range of tasks, modern data centers also heavily rely on another crucial processing unit for specialized workloads.  08:54 Lois: We’ve spoken a lot about CPUs, but our conversation would be incomplete without understanding what a Graphics Processing Unit is and why it’s important. What can you tell us about GPUs, Orlando?  Orlando: A GPU or Graphics Processing Unit is distinct from a CPU. While the CPU is a generalist excelling at sequential processing and managing a wide variety of tasks, the GPU is a specialist.  It is designed specifically for parallel compute heavy tasks. This means it can perform many calculations simultaneously, making it incredibly efficient for workloads like rendering graphics, scientific simulations, and especially in areas like machine learning and artificial intelligence, where massive parallel computation is required.  In the modern data center, GPUs are increasingly vital for accelerating these specialized, data intensive workloads.   09:58 Nikita: Besides the CPU and GPU, there’s another key component that collaborates with these processors to facilitate efficient data access. What role does Random Access Memory play in all of this?  Orlando: The core function of RAM is to provide faster access to information in use. Imagine your computer or server needing to retrieve data from a long-term storage device, like a hard drive. This process can be relatively slow.  RAM acts as a temporary high-speed buffer. When your CPU or GPU needs data, it first checks RAM. If the data is there, it can be accessed almost instantaneously, significantly speeding up operations.  This rapid access to frequently used data and progra

AI is reshaping industries at a rapid pace, but as its influence grows, so do the ethical concerns that come with it.   This episode examines how AI is being applied across sectors such as healthcare, finance, and retail, while also exploring the crucial issue of ensuring that these technologies align with human values.   In this conversation, Lois Houston and Nikita Abraham are joined by Hemant Gahankari, Senior Principal OCI Instructor, who emphasizes the importance of fairness, inclusivity, transparency, and accountability in AI systems.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------- Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about how Oracle integrates AI capabilities into its Fusion Applications to enhance business workflows, and we focused on Predictive, Generative, and Agentic AI. Lois: Today, we’ll discuss the various applications of AI. This is the final episode in our AI series, and before we close, we’ll also touch upon ethical and responsible AI.  01:01 Nikita: Taking us through all of this is Senior Principal OCI Instructor Hemant Gahankari. Hi Hemant! AI is pretty much everywhere today. So, can you explain how it is being used in industries like retail, hospitality, health care, and so on?  Hemant: AI isn't just for sci-fi movies anymore. It's helping doctors spot diseases earlier and even discover new drugs faster. Imagine an AI that can look at an X-ray and say, hey, there is something sketchy here before a human even notices. Wild, right? Banks and fintech companies are all over AI. Fraud detection. AI has got it covered. Those robo advisors managing your investments? That's AI too. Ever noticed how e-commerce companies always seem to know what you want? That's AI studying your habits and nudging you towards that next purchase or binge watch. Factories are getting smarter. AI predicts when machines will fail so they can fix them before everything grinds to a halt. Less downtime, more efficiency. Everyone wins. Farming has gone high tech. Drones and AI analyze crops, optimize water use, and even help with harvesting. Self-driving cars get all the hype, but even your everyday GPS uses AI to dodge traffic jams. And if AI can save me from sitting in bumper-to-bumper traffic, I'm all for it. 02:40 Nikita: Agreed! Thanks for that overview, but let’s get into specific scenarios within each industry.  Hemant: Let us take a scenario in the retail industry-- a retail clothing line with dozens of brick-and-mortar stores. Maintaining proper inventory levels in stores and regional warehouses is critical for retailers. In this low-margin business, being out of a popular product is especially challenging during sales and promotions. Managers want to delight shoppers and increase sales but without overbuying. That's where AI steps in. The retailer has multiple information sources, ranging from point-of-sale terminals to warehouse inventory systems. This data can be used to train a forecasting model that can make predictions, such as demand increase due to a holiday or planned marketing promotion, and determine the time required to acquire and distribute the extra inventory. Most ERP-based forecasting systems can produce sophisticated reports. A generative AI report writer goes further, creating custom plain-language summaries of these reports tailored for each store, instructing managers about how to maximize sales of well-stocked items while mitigating possible shortages. 04:11 Lois: Ok. How is AI being used in the hospitality sector, Hemant? Hemant: Let us take an example of a hotel chain that depends on positive ratings on social media and review websites. One common challenge they face is keeping track of online reviews, leading to missed opportunities to engage unhappy customers complaining on social media. Hotel managers don't know what's being said fast enough to address problems in real-time. Here, AI can be used to create a large data set from the tens of thousands of previously published online reviews. A textual language AI system can perform a sentiment analysis across the data to determine a baseline that can be periodically re-evaluated to spot trends. Data scientists could also build a model that correlates these textual messages and their sentiments against specific hotel locations and other factors, such as weather. Generative AI can extract valuable suggestions and insights from both positive and negative comments. 05:27 Nikita: That’s great. And what about Financial Services? I know banks use AI quite often to detect fraud. Hemant: Unfortunately, fraud can creep into any part of a bank's retail operations. Fraud can happen with online transactions, from a phone or browser, and offsite ATMs too. Without trust, banks won't have customers or shareholders. Excessive fraud and delays in detecting it can violate financial industry regulations. Fraud detection combines AI technologies, such as computer vision to interpret scanned documents, document verification to authenticate IDs like driver's licenses, and machine learning to analyze patterns. These tools work together to assess the risk of fraud in each transaction within seconds. When the system detects a high risk, it triggers automated responses, such as placing holds on withdrawals or requesting additional identification from customers, to prevent fraudulent activity and protect both the business and its client. 06:42 Nikita: Wow, interesting. And how is AI being used in the health industry, especially when it comes to improving patient care? Hemant: Medical appointments can be frustrating for everyone involved—patients, receptionists, nurses, and physicians. There are many time-consuming steps, including scheduling, checking in, interactions with the doctors, checking out, and follow-ups. AI can fix this problem through electronic health records to analyze lab results, paper forms, scans, and structured data, summarizing insights for doctors with the latest research and patient history. This helps practice reduced costs, boost earnings, and deliver faster, more personalized care. 07:32 Lois: Let’s take a look at one more industry. How is manufacturing using AI? Hemant: A factory that makes metal parts and other products use both visual inspections and electronic means to monitor product quality. A part that fails to meet the requirements may be reworked or repurposed, or it may need to be scrapped. The factory seeks to maximize profits and throughput by shipping as much good material as possible, while minimizing waste by detecting and handling defects early. The way AI can help here is with the quality assurance process, which creates X-ray images. This data can be interpreted by computer vision, which can learn to identify cracks and other weak spots, after being trained on a large data set. In addition, problematic or ambiguous data can be highlighted for human inspectors. 08:36 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest tech. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 09:20 Nikita: Welcome back! AI can be used effectively to automate a variety of tasks to improve productivity, efficiency, cost savings. But I’m sure AI has its constraints too, right? Can you talk about what happens if AI isn’t able to echo human ethics?  Hemant: AI can fail due to lack of ethics.  AI can spot patterns, not make moral calls. It doesn't feel guilt, understand context, or take responsibility. That is still up to us.  Decisions are only as good as the data behind them. For example, health care AI underdiagnosing women because research data was mostly male. Artificial narrow intelligence tends to automate discrimination at scale. Recruiting AI downgraded resumes just because it had a word "women's" (for example, women's chess club). Who is responsible when AI fails? For example, if a self-driving car hits someone, we cannot blame the car. Then who owns the failure? The programmer? The CEO? Can we really trust corporations or governments having programmed the use of AI not to be evil correctly? So, it's clear that AI needs oversight to function smoothly. 10:48 Lois: So, Hemant, how can we design AI in ways that respect and reflect human values? Hemant: Think of ethics like a tree. It needs all parts working together. Roots represent intent. That is our values and principles. The trunk stands for safeguards, our systems, and structures. And the branches are the outcomes we aim for. If the roots are shallow, the tree falls. If the trunk is weak, damage seeps through. The health of roots and trunk shapes the strength of our ethical outcomes. Fairness means nothing without ethical intent behind it. For example, a bank promotes its loan algorithm as fair. But it uses zip codes in decision-making, effectively penalizing people based on race. That's not fairness. Tha

Want to make AI work for your business? In today’s episode, Lois Houston and Nikita Abraham continue their discussion of AI in Oracle Fusion Applications by focusing on three key AI capabilities: predictive, generative, and agentic.   Joining them is Principal Instructor Yunus Mohammed, who explains how predictive, generative, and agentic AI can optimize efficiency, support decision-making, and automate tasks—all without requiring technical expertise.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we explored the essential components of the Oracle AI stack and spoke about Oracle’s suite of AI services.  Nikita: Yeah, and in today’s episode, we’re going to go down a similar path and take a closer look at the AI functionalities within Oracle Fusion Applications. 00:53 Lois: With us today is Principal Instructor Yunus Mohammed. Hi Yunus! It’s lovely to have you back with us. For anyone who doesn’t already know, what are Oracle Fusion Cloud Applications?  Yunus: Oracle Fusion Applications are a suite of cloud-based enterprise applications designed to run for your business across finance, HR, supply chain, sales, services and more, all on a unified platform. They are designed to help enterprises operate smarter, faster by embedding AI directly into business process. That means better forecasts in finance, faster hiring decisions in HR, and optimized supply chains, and more personalized customer experience.  01:42 Nikita: And we know they’ve been built for today's fast-paced, AI-driven business environment. So, what are the different functional pillars within Oracle Fusion Apps? Yunus: The first one is the ERP, Enterprise Resource Planning, which supports financials, procurements, and project management. It's the backbone of many organizations, or day-to-day operations. HCM or Human Capital Management, handles workforce-related processes such as hiring, payroll, performance, and talent development, helping HR teams operate more efficiently. SCM, the Supply Chain Management, enables businesses to manage their logistics, inventory, and suppliers and manufacturers in the business. It's particularly critical in industries with complex operations like retail and manufacturing. The CX, which is the Customer Experience, covers the full customer life cycle, which includes sales, marketing, and service. These models help the businesses connect with their customers more personally and proactively, whether through the targeted campaigns or responsive support.  03:02 Lois: Yunus, what sets Fusion apart? Yunus: What sets Fusion apart is how these applications work seamlessly together. They share data natively and continuously improve with AI and automation, giving you not just tools, but intelligence at scale.  Oracle applications are built to be AI first, with a complete suite of finance, supply chain, manufacturing, HR, sales, service, and marketing, that is tightly coupled with our industry and data intelligence applications. The easiest and the most effective way to start building your organization’s AI muscle is with AI embedded in Fusion applications. For example, if the customer needs to return a defective product, the service representative simply clicks on Ask Oracle for the answers. Since the AI agent is embedded in the application, it has contextual information about the customer, the order, and any special service, contract, or any other feature that is required for this process. The AI agent automatically figures out the return policy, including the options to send a replacement product immediately or offer a discount for the inconvenience, and also expedite shipping. Another AI agent sends a personalized email confirming details of the return, and different AI agent creates the replacement order for fulfillment and shipping. Our AI-embedded Fusion Applications can automate an end-to-end business process from service request to return order to fulfillment and shipping and then accounting.  These are pre-built and tested so that all the worry and hard work is removed from the implementation point of view. They cover the core workflows. Basically, they address tasks that form part of the organization's core workflow. User requires no technical knowledge in the scenarios.  05:16 Lois: That’s great! So, you don’t need to be an AI expert or a data scientist to get going. Yunus: The outcomes are super fast in business softwares and context is everything. Just having the right information isn't enough. This is about having the information in the right place at the right time for it to be instantly actionable. They are ready from day one and can be optimized over time. They are powerful out of the box and only get better with day-to-day processes and performance. 05:55 Are you working towards an Oracle Certification this year? Join us at one of our certification prep live events in the Oracle University Learning Community. Get insider tips from seasoned experts and learn from others who have already taken their certifications. Go to community.oracle.com/ou to jump-start your journey towards certification today!  06:20 Nikita: Welcome back! So, when we talk about the AI capabilities in Fusion apps, I know we have different types. Can you tell us more about them?  Yunus: Predictive AI is where it all started. These models analyze historical patterns and data to anticipate what might happen next. For example, predicting employee attrition, forecasting demand in supply chain, or flagging potential late payments in finance workflows. These are embedded into business processes to surface insights before action is needed. Then we have got the generative AI, which takes this a step more further. Instead of just providing insights, it creates content, such as auto-generating job descriptions, summarizing performance reviews, or even crafting draft responses to supplier queries. This saves time and boosts productivity across functions like HR, CX, and procurement. Last but not the least, we have got the agentic AI, which is the most advanced layer. These agents don't just provide suggestions, they take actions on behalf of the users. Think of an agent that not only recommends actions in a workflow, but also executes them, creating tasks, filling tickets, updating systems, and communicating with stakeholders, all autonomously but under user control. And importantly, many business scenarios today benefit from a blend of these types. For example, an AI assistant in Fusion HCM might predict employees turnover, which is predictive AI, generates tailored retention plans, which is generative, and it is generative AI, and initiate outreach or next steps, which is done by the process of agents, which is called agentic AI. So, Oracle integrates these capabilities in a harmonious way, enabling users to act faster, personalize at scale, and drive better business outcomes.  08:39 Lois: Ok, let’s get into the specifics. How does Oracle use predictive AI across its Fusion apps, helping businesses anticipate what’s coming and act proactively.  Yunus: So in HCM, things like recommended jobs, in this, candidates visiting a potential employer’s website encountered an improved online experience, whereby if they have uploaded their resumes, they will be shown job opportunities that match their skills and experience mix. This helps candidates who are unsure what to search by showing them roles and titles they may not have considered. Time to hire provides an estimated as to how long it will take for an HR team to fill an open role, but this is really useful not only in terms of planning, recruitment, but also in terms of understanding whether you might need some temporary cover and for how long will it actually take the process to complete. In the process of supply chain management, the predictive AI is leveraged to revolutionize transit time and estimated time of arrival, which is called as the predictive analysis, enhancing efficiency, and optimizing operations. It can flag abnormal patterns in supply or inventory. For example, if a batch of parts is behaving differently in the production line and predict future demands, helping avoid overstocking or stockouts is a process that can be done by using the SCM predictive analysis or predictive AI. In ERPs, where you can audit your expenses, plan for future expenses, and do dynamic discounting for vendors who are likely to accept earlier payments or earlier payment discounts, it can also speed up reimbursements by automated expense entries. In CX, you have the options to go with adaptive intelligence for sales, which helps representatives prioritize the leads and the likelihood that a specific lead will close, helping representatives focus their time and effort. So predictive scheduling and routing in service delivery ensures that the right resource is assigned to the right customer at the right time, boosting operational efficiency and customer satisfaction, also known as fatigue analysis. 11:23 Lois: Now let’s shift our focus to generative AI. How does Oracle implement generative AI across HCM, ERP, Supply Chain, and CX? Yunus: So, in HCM,

In this episode, Lois Houston and Nikita Abraham are joined by Principal Instructor Yunus Mohammed to explore Oracle’s approach to enterprise AI. The conversation covers the essential components of the Oracle AI stack and how each part, from the foundational infrastructure to business-specific applications, can be leveraged to support AI-driven initiatives.   They also delve into Oracle’s suite of AI services, including generative AI, language processing, and image recognition.     AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   -------------------------------------------------------------   Episode Transcript:  00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we discussed why the decision to buy or build matters in the world of AI deployment. Lois: That’s right, Niki. Today is all about the Oracle AI stack and how it empowers not just developers and data scientists, but everyday business users as well. Then we’ll spend some time exploring Oracle AI services in detail.  01:00 Nikita: Yunus Mohammed, our Principal Instructor, is back with us today. Hi Yunus! Can you talk about the different layers in Oracle’s end-to-end AI approach? Yunus: The first base layer is the foundation of AI infrastructure, the powerful compute and storage layer that enables scalable model training and inferences. Sitting above the infrastructure, we have got the data platform. This is where data is stored, cleaned, and managed. Without a reliable data foundation, AI simply can't perform. So base of AI is the data, and the reliable data gives more support to the AI to perform its job. Then, we have AI and ML services. These provide ready-to-use tools for building, training, and deploying custom machine learning models. Next, to the AI/ML services, we have got generative AI services. This is where Oracle enables advanced language models and agentic AI tools that can generate content, summarize documents, or assist users through chat interfaces. Then, we have the top layer, which is called as the applications, things like Fusion applications or industry specific solutions where AI is embedded directly into business workflows for recommendations, forecasting or customer support. Finally, Oracle integrates with a growing ecosystem of AI partners, allowing organizations to extend and enhance their AI capabilities even further. In short, Oracle doesn't just offer AI as a feature. It delivers it as a full stack capability from infrastructure to the layer of applications. 02:59 Nikita: Ok, I want to get into the core AI services offered by Oracle Cloud Infrastructure. But before we get into the finer details, broadly speaking, how do these services help businesses? Yunus: These services make AI accessible, secure, and scalable, enabling businesses to embed intelligence into workflows, improve efficiency, and reduce human effort in repetitive or data-heavy tasks. And the best part is, Oracle makes it easy to consume these through application interfaces, APIs, software development kits like SDKs, and integration with Fusion Applications. So, you can add AI where it matters without needing a data scientist team to do that work.  03:52 Lois: So, let’s get down to it. The first core service is Oracle's Generative AI service. What can you tell us about it?  Yunus: This is a fully managed service that allows businesses to tap into the power of large language models. You can actually work with these models from scratch to a well-defined develop model. You can use these models for a wide range of use cases like summarizing text, generating content, answering questions, or building AI-powered chat interfaces.  04:27 Lois: So, what will I find on the OCI Generative AI Console? Yunus: OCI Generative AI Console highlights three key components. The first one is the dedicated AI cluster. These are GPU powered environments used to fine tune and host your own custom models. It gives you control and performance at scale. Then, the second point is the custom models. You can take a base language model and fine tune it using your own data, for example, company manuals or HR policies or customer interactions, which are your own personal data. You can use this to create a model that speaks your business language. And last but not the least, the endpoints. These are the interfaces through which your application connect to the model. Once deployed, your app can query the model securely and at different scales, and you don't need to be a developer to get started. Oracle offers a playground, which is a non-core environment where you can try out models, craft parameters, and test responses interactively. So overall, the generative AI service is designed to make enterprise-grade AI accessible and customizable. So, fitting directly into business processes, whether you are building a smart assistant or you're automating the content generation process.  06:00 Lois: The next key service is OCI Generative AI Agents. Can you tell us more about it?  Yunus: OCI Generative AI agents combines a natural language interface with generative AI models and enterprise data stores to answer questions and take actions. The agent remembers the context, uses previous interactions, and retrieves deeper product speech details. They aren't just static chat bots. They are context aware, grounded in business data, and able to handle multi-turns, follow-up queries with relevant accurate responses, and driving productivity and decision-making across departments like sales, support, or operations. 06:54 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest tech. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 07:37 Nikita: Welcome back! Yunus, let’s move on to the OCI Language service.  Yunus: OCI Language helps business understand and process natural language at scale. It uses pretrained models, which means they are already trained on large industry data sets and are ready to be used right away without requiring AI expertise. It detects over 100 languages, including English, Japanese, Spanish, and more. This is great for global business that receive multilingual inputs from customers. It works with identity sentiments. For different aspects of the sentence, for example, in a review like, “The food was great, but the service sucked,” OCI Language can tell that food has a positive sentiment while service has a negative one. This is called aspect-based sentiment analysis, and it is more insightful than just labeling the entire text as positive or negative. Then we have got to identify key phrases representing important ideas or subjects. So, it helps in extracting these key phrases, words, or terms that capture the core messages. They help automate tagging, summarizing, or even routing of content like support tickets or emails.  In real life, the businesses are using this for customer feedback analysis, support ticket routing, social media monitoring, and even regulatory compliances.  09:21 Nikita: That’s fantastic. And what about the OCI Speech service?  Yunus: The OCI Speech is an AI service that transcribes speech to text. Think of it as an AI-powered transcription engine that listens to the spoken English, whether in audio or video files, and turns it into usable and searchable and readable text. It provides timestamps, so you know exactly when something was said. A valuable feature for reviewing legal discussions, media footages, or compliance audits. OCI Speech even understands different speakers. You don't need to train this from scratch. It is pre-trained model hosted on an API. Just send your audio to the service, and you get an accurate timestamp text back in return. 10:17 Lois: I know we also have a service for object detection… called OCI Vision?  Yunus: OCI Vision uses pretrained, deep learning models to understand and analyze visual content. Just like a human might, you can upload an image or videos, and the AI can tell you what is in it and where they might be useful. There are two primary use cases, which you can use this particular OCI Vision for. One is for object detection. You have got a red color car. So OCI Vision is not just identifying that’s a car. It is detecting and labeling parts of the car too, like the bumper, the wheels, the design components. This is a critical in industries like manufacturing, retail, or logistics. For example, in quality control, OCI Vision can scan product images to detect missing or defective parts automatically.  Then we have got the image classification. This is useful in scenarios like automated tagging of photos, managing digital assets, classifying this particular scene or context of this particular scene. So basically, when we talk about OCI Vision, which is actually a fully managed, no complex model training is required for this particular service. It's available via API. It is also working with defining their own custom model for working with the env

How do you decide whether to buy a ready-made AI solution or build one from the ground up? The choice is more than just a technical decision; it’s about aligning AI with your business goals.   In this episode, Lois Houston and Nikita Abraham are joined by Principal Instructor Yunus Mohammed to examine the critical factors influencing the buy vs. build debate. They explore real-world examples where businesses must weigh speed, customization, and long-term strategy. From a startup using a SaaS chatbot to a bank developing a custom fraud detection model, Yunus provides practical insights on when to choose one approach over the other.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Last week, we spoke about the key stages in a typical AI workflow and how data quality, feedback loops, and business goals influence AI success. 00:50 Nikita: In today’s episode, we’re going to explore whether you should buy or build AI apps. Joining us again is Principal Instructor Yunus Mohammed. Hi Yunus, let’s jump right in. Why does the decision of buy versus build matter? Yunus: So when we talk about buy versus build matters, we need to consider the strategic business decisions over here. They are related to the strategic decisions which the business makes, and it is evaluated in the decision lens. So the center of the decision lens is the business objective, which identifies what are we trying to solve. Then evaluate our constraints based on that particular business objective like the cost, the time, and the talent. And finally, we can decide whether we need to buy or build. But remember, there is no single correct answer. What's right for one business may not be working for the other one. 01:54 Lois: OK, can you give us examples of both approaches? Yunus: The first example where we have got a startup using a SaaS AI chatbot. Now, being a startup, they have to choose a ready-made solution, which is an AI chatbot. Now, the question is, why did they do this? Because speed and simplicity mattered more than deep customization that is required for the chatbot. So, their main aim was to have it ready in short period of time and make it more simpler. And this actually lead them to get to the market fast with low upfront cost and minimal technical complexities. But in some situations, it might be different. Like, your bank, which needs to build a fraud model. It cannot be outsourced or got from the shelf. So, they build a custom model in-house. With this custom model, they actually have a tighter control, and it is tuned to their standards. And it is created by their experts. So these two generic examples, the chatbot and the fraud model example, helps you in identifying whether I should go for a SaaS product with simple choice of selecting an existing LLM endpoint and not making any changes. Or should I go with model depending on my business and organization requirement and fine tuning that model later to define a better implementation of the scenarios or conditions that I want to do which are specific to my organization. So here you decide with the reference whether I want it to be done faster, or whether I want to be more customized to my organization. So buy it, when it is generic, or build when it is strategic. The SaaS, which is basically software as a service, refers to ready to use cloud-based applications that you access via internet. You can log into the platform and use the built-in AI, there's no setup requirement for those. Real-world examples can be Oracle Fusion apps with AI features enabled. So in-house integration means embedding AI with my own requirements into your own systems, often using custom APIs, data pipelines, and hosting it. It gives you more flexibility but requires a lot of resources and expertise. So real-world example for this scenario can be a logistics heavy company, which is integrating a customer support model into their CX. 04:41 Lois: But what are the pros and cons of each approach? Yunus: So, SaaS and Fusion Applications, basically, they offer fast deployment with little to no coding required, making them ideal for business looking to get started quickly and faster. And they typically come with lower upfront costs and are maintained by vendor, which means updates, security, support are handled externally. However, there are limited customizations and are best suited for common, repeatable use cases. Like, it can be a standard chatbot, or it can be reporting tools, or off the shelf analytics that you want to use. But the in-house or custom integration, you have more control, but it takes longer to build and requires a higher initial investment. The in-house or custom integration approach allows full customization of the features and the workflows, enabling you to design and tailor the AI system to your specific needs. 05:47 Nikita: If you're weighing the choice between buying or building, what are the critical business considerations you'd need to take into account? Yunus: So let's take one of the key business consideration which is time to market. If your goal is to launch fast, maybe you're a startup trying to gain traction quickly, then a prebuilt plug and play AI solution, for example, a chatbot or any other standard analytical tool, might be your best bet. But if you have time and you are aiming for precision, a custom model could be worth the wait. Prebuilt SaaS tools usually have lower upfront costs and a subscription model. It works with putting subscriptions. Custom solutions, on the other hand, may require a bigger investment upfront. In development, you require high talent and infrastructures, but could offer cost savings in the long run. So, ask yourself a question here. Is this AI helping us stand out in the market? If the answer is yes, you may want to build something which is your proprietary. For example, an organization would use a generic recommendation engine. It's a part of their secret sauce. Some use cases require flexibility, like you want to tailor the rules to match your specific risk criteria. So, under that scenarios, you will go for customizing. So, you will go with off the shelf solutions may not give you deep enough requirements that you want to evaluate. So, you get those and you try to customize those. You can go for customization of your AI features. The other important key business consideration is the talent and expertise that your organization have. So, the question that you need to ask in the organization is, do you have an internal team who is well versed in developing AI solutions for you? Or do you have access to one of the teams which can help you build your own proprietary products? If not, you'll go with SaaS. If you do have, then building could unlock greater control over your AI features and AI models. The next core component is your security and data privacy. If you're handling sensitive information, like for example, the health care or finance data, you might not want to send your data to the third-party tools. So in-house models offer better control over data security and compliance. When we leverage a model, it could be a prebuilt or custom model. 08:50 Oracle University is proud to announce three brand new courses that will help your teams unlock the power of Redwood—the next generation design system. Redwood enhances the user experience, boosts efficiency, and ensures consistency across Oracle Fusion Cloud Applications. Whether you're a functional lead, configuration consultant, administrator, developer, or IT support analyst, these courses will introduce you to the Redwood philosophy and its business impact. They’ll also teach you how to use Visual Builder Studio to personalize and extend your Fusion environment. Get started today by visiting mylearn.oracle.com.  09:31 Nikita: Welcome back! So, getting back to what you were saying before the break, what are pre-built and custom models? Yunus: A prebuilt model is an AI solution that has already been trained by someone else, typically a tech provider. It can be used to perform a specific task like recognizing images, translating text, or detecting sentiments. You can think of it like buying a preassembled appliance. You plug it in, configure a few settings, and it's ready to use. You don't need to know how the internal parts work. You benefit from the speed, ease, and reliability of this particular model, which is a prebuilt model. But you can't easily change how it works under the hood. Whereas, a custom model is an AI solution that your organization designs and trains and tunes specifically for their business problems using their own data. You can think of it like designing your own suit. It takes more time and effort to create. It is built to your exact measurements and needs. And you have full control over how it performs and evolves. 10:53 Lois: So, when would you choose a pre-built versus a custom model? Yunus: Depending on speed, simplicity, control, and customization, you can decide on using a prebuilt or to create a custom model. Prebuilt models are like plug and play solutions. Think of tools like Google Translate for languages. OpenAI APIs fo

Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI’s real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we’re going to look at the key stages in a typical AI workflow. We’ll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University.  01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model?  Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately.  After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting.  So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results.  04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development?  Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data.  Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches?  Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data?  Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart.  08:23 Lois: So, we’ve established that collecting the right data is non-negotiable for success. Then comes preparing it, right?  Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format.  10:31 Lois: And does each AI system have a different way of preparing data?  Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest tech. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem?  Yunus: Just like a business uses different dashboards for marketing versus finance, in

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they discuss the transformative world of Generative AI. Together, they uncover the ways in which generative AI agents are changing the way we interact with technology, automating tasks and delivering new possibilities.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead of Editorial Services.   Nikita: Hi everyone! Last week was Part 2 of our conversation on core AI concepts, where we went over the basics of data science. In Part 3 today, we’ll look at generative AI and gen AI agents in detail. To help us with that, we have Himanshu Raj, Principal AI/ML Instructor. Hi Himanshu, what’s the difference between traditional AI and generative AI?  01:01 Himanshu: So until now, when we talked about artificial intelligence, we usually meant models that could analyze information and make decisions based on it, like a judge who looks at evidence and gives a verdict. And that's what we call traditional AI that's focused on analysis, classification, and prediction.  But with generative AI, something remarkable happens. Generative AI does not just evaluate. It creates. It's more like a storyteller who uses knowledge from the past to imagine and build something brand new. For example, instead of just detecting if an email is spam, generative AI could write an entirely new email for you.  Another example, traditional AI might predict what a photo contains. Generative AI, on the other hand, creates a brand-new photo based on description. Generative AI refers to artificial intelligence models that can create entirely new content, such as text, images, music, code, or video that resembles human-made work.  Instead of simple analyzing or predicting, generative AI produces something original that resembles what a human might create.   02:16 Lois: How did traditional AI progress to the generative AI we know today?  Himanshu: First, we will look at small supervised learning. So in early days, AI models were trained on small labeled data sets. For example, we could train a model with a few thousand emails labeled spam or not spam. The model would learn simple decision boundaries. If email contains, "congratulations," it might be spam. This was efficient for a straightforward task, but it struggled with anything more complex.  Then, comes the large supervised learning. As the internet exploded, massive data sets became available, so millions of images, billions of text snippets, and models got better because they had much more data and stronger compute power and thanks to advances, like GPUs, and cloud computing, for example, training a model on millions of product reviews to predict customer sentiment, positive or negative, or to classify thousands of images in cars, dogs, planes, etc.  Models became more sophisticated, capturing deeper patterns rather than simple rules. And then, generative AI came into the picture, and we eventually reached a point where instead of just classifying or predicting, models could generate entirely new content.  Generative AI models like ChatGPT or GitHub Copilot are trained on enormous data sets, not to simply answer a yes or no, but to create outputs that look and feel like human made. Instead of judging the spam or sentiment, now the model can write an article, compose a song, or paint a picture, or generate new software code.  03:55 Nikita: Himanshu, what motivated this sort of progression?   Himanshu: Because of the three reasons. First one, data, we had way more of it thanks to the internet, smartphones, and social media. Second is compute. Graphics cards, GPUs, parallel computing, and cloud systems made it cheap and fast to train giant models.  And third, and most important is ambition. Humans always wanted machines not just to judge existing data, but to create new knowledge, art, and ideas.   04:25 Lois: So, what’s happening behind the scenes? How is gen AI making these things happen?  Himanshu: Generative AI is about creating entirely new things across different domains. On one side, we have large language models or LLMs.  They are masters of generating text conversations, stories, emails, and even code. And on the other side, we have diffusion models. They are the creative artists of AI, turning text prompts into detailed images, paintings, or even videos.  And these two together are like two different specialists. The LLM acts like a brain that understands and talks, and the diffusion model acts like an artist that paints based on the instructions. And when we connect these spaces together, we create something called multimodal AI, systems that can take in text and produce images, audio, or other media, opening a whole new range of possibilities.  It can not only take the text, but also deal in different media options. So today when we say ChatGPT or Gemini, they can generate images, and it's not just one model doing everything. These are specialized systems working together behind the scenes.  05:38 Lois: You mentioned large language models and how they power text-based gen AI, so let’s talk more about them. Himanshu, what is an LLM and how does it work?  Himanshu: So it's a probabilistic model of text, which means, it tries to predict what word is most likely to come next based on what came before.  This ability to predict one word at a time intelligently is what builds full sentences, paragraphs, and even stories.  06:06 Nikita: But what’s large about this? Why’s it called a large language model?   Himanshu: It simply means the model has lots and lots of parameters. And think of parameters as adjustable dials the model fine tuned during learning.  There is no strict rule, but today, large models can have billions or even trillions of these parameters. And the more the parameters, more complex patterns, the model can understand and can generate a language better, more like human.  06:37 Nikita: Ok… and image-based generative AI is powered by diffusion models, right? How do they work?  Himanshu: Diffusion models start with something that looks like pure random noise.  Imagine static on an old TV screen. No meaningful image at all. From there, the model carefully removes noise step by step to create something more meaningful and think of it like sculpting a statue. You start with a rough block of stone and slowly, carefully you chisel away to reveal a beautiful sculpture hidden inside.  And in each step of this process, the AI is making an educated guess based on everything it has learned from millions of real images. It's trying to predict.   07:24 Stay current by taking the 2025 Oracle Fusion Cloud Applications Delta Certifications. This is your chance to demonstrate your understanding of the latest features and prove your expertise by obtaining a globally recognized certification, all for free! Discover the certification paths, use the resources on MyLearn to prepare, and future-proof your skills. Get started now at mylearn.oracle.com.  07:53 Nikita: Welcome back! Himanshu, for most of us, our experience with generative AI is with text-based tools like ChatGPT. But I’m sure the uses go far beyond that, right? Can you walk us through some of them?  Himanshu: First one is text generation. So we can talk about chatbots, which are now capable of handling nuanced customer queries in banking travel and retail, saving companies hours of support time. Think of a bank chatbot helping a customer understand mortgage options or virtual HR Assistant in a large company, handling leave request. You can have embedding models which powers smart search systems.  Instead of searching by keywords, businesses can now search by meaning. For instance, a legal firm can search cases about contract violations in tech and get semantically relevant results, even if those exact words are not used in the documents.  The third one, for example, code generation, tools like GitHub Copilot help developers write boilerplate or even functional code, accelerating software development, especially in routine or repetitive tasks. Imagine writing a waveform with just a few prompts.  The second application, is image generation. So first obvious use is art. So designers and marketers can generate creative concepts instantly. Say, you need illustrations for a campaign on future cities. Generative AI can produce dozens of stylized visuals in minutes.  For design, interior designers or architects use it to visualize room layouts or design ideas even before a blueprint is finalized. And realistic images, retail companies generate images of people wearing their clothing items without needing real models or photoshoots, and this reduces the cost and increase the personalization.  Third application is multimodal systems, and these are combined systems that take one kind of input or a combination of different inputs and produce different kind of outputs, or can even combine various kinds, be it text image in both input and output.  Text to image It's being used in e-commerce, movie concept art, and educational content creation. For text to video, this is still in early days, but imagine creating a product explainer video just by typing out t

In this episode, Lois Houston and Nikita Abraham continue their discussion on AI fundamentals, diving into Data Science with Principal AI/ML Instructor Himanshu Raj. They explore key concepts like data collection, cleaning, and analysis, and talk about how quality data drives impactful insights.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we began our exploration of core AI concepts, specifically machine learning and deep learning. I’d really encourage you to go back and listen to the episode if you missed it.   00:52 Lois: Yeah, today we’re continuing that discussion, focusing on data science, with our Principal AI/ML Instructor Himanshu Raj.  Nikita: Hi Himanshu! Thanks for joining us again. So, let’s get cracking! What is data science?  01:06 Himanshu: It's about collecting, organizing, analyzing, and interpreting data to uncover valuable insights that help us make better business decisions. Think of data science as the engine that transforms raw information into strategic action.  You can think of a data scientist as a detective. They gather clues, which is our data. Connect the dots between those clues and ultimately solve mysteries, meaning they find hidden patterns that can drive value.  01:33 Nikita: Ok, and how does this happen exactly?  Himanshu: Just like a detective relies on both instincts and evidence, data science blends domain expertise and analytical techniques. First, we collect raw data. Then we prepare and clean it because messy data leads to messy conclusions. Next, we analyze to find meaningful patterns in that data. And finally, we turn those patterns into actionable insights that businesses can trust.  02:00 Lois: So what you’re saying is, data science is not just about technology; it's about turning information into intelligence that organizations can act on. Can you walk us through the typical steps a data scientist follows in a real-world project?  Himanshu: So it all begins with business understanding. Identifying the real problem we are trying to solve. It's not about collecting data blindly. It's about asking the right business questions first. And once we know the problem, we move to data collection, which is gathering the relevant data from available sources, whether internal or external.  Next one is data cleaning. Probably the least glamorous but one of the most important steps. And this is where we fix missing values, remove errors, and ensure that the data is usable. Then we perform data analysis or what we call exploratory data analysis.  Here we look for patterns, prints, and initial signals hidden inside the data. After that comes the modeling and evaluation, where we apply machine learning or deep learning techniques to predict, classify, or forecast outcomes. Machine learning, deep learning are like specialized equipment in a data science detective's toolkit. Powerful but not the whole investigation.  We also check how good the models are in terms of accuracy, relevance, and business usefulness. Finally, if the model meets expectations, we move to deployment and monitoring, putting the model into real world use and continuously watching how it performs over time.  03:34 Nikita: So, it’s a linear process?  Himanshu: It's not linear. That's because in real world data science projects, the process does not stop after deployment. Once the model is live, business needs may evolve, new data may become available, or unexpected patterns may emerge.  And that's why we come back to business understanding again, defining the questions, the strategy, and sometimes even the goals based on what we have learned. In a way, a good data science project behaves like living in a system which grows, adapts, and improves over time. Continuous improvement keeps it aligned with business value.   Now, think of it like adjusting your GPS while driving. The route you plan initially might change as new traffic data comes in. Similarly, in data science, new information constantly help refine our course. The quality of our data determines the quality of our results.   If the data we feed into our models is messy, inaccurate, or incomplete, the outputs, no matter how sophisticated the technology, will be also unreliable. And this concept is often called garbage in, garbage out. Bad input leads to bad output.  Now, think of it like cooking. Even the world's best Michelin star chef can't create a masterpiece with spoiled or poor-quality ingredients. In the same way, even the most advanced AI models can't perform well if the data they are trained on is flawed.  05:05 Lois: Yeah, that's why high-quality data is not just nice to have, it’s absolutely essential. But Himanshu, what makes data good?   Himanshu: Good data has a few essential qualities. The first one is complete. Make sure we aren't missing any critical field. For example, every customer record must have a phone number and an email. It should be accurate. The data should reflect reality. If a customer's address has changed, it must be updated, not outdated. Third, it should be consistent. Similar data must follow the same format. Imagine if the dates are written differently, like 2024/04/28 versus April 28, 2024. We must standardize them.   Fourth one. Good data should be relevant. We collect only the data that actually helps solve our business question, not unnecessary noise. And last one, it should be timely. So data should be up to date. Using last year's purchase data for a real time recommendation engine wouldn't be helpful.  06:13 Nikita: Ok, so ideally, we should use good data. But that’s a bit difficult in reality, right? Because what comes to us is often pretty messy. So, how do we convert bad data into good data? I’m sure there are processes we use to do this.  Himanshu: First one is cleaning. So this is about correcting simple mistakes, like fixing typos in city names or standardizing dates.  The second one is imputation. So if some values are missing, we fill them intelligently, for instance, using the average income for a missing salary field. Third one is filtering. In this, we remove irrelevant or noisy records, like discarding fake email signups from marketing data. The fourth one is enriching. We can even enhance our data by adding trusted external sources, like appending credit scores from a verified bureau.  And the last one is transformation. Here, we finally reshape data formats to be consistent, for example, converting all units to the same currency. So even messy data can become usable, but it takes deliberate effort, structured process, and attention to quality at every step.  07:26 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest technology. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 08:10 Nikita: Welcome back! Himanshu, we spoke about how to clean data. Now, once we get high-quality data, how do we analyze it?  Himanshu: In data science, there are four primary types of analysis we typically apply depending on the business goal we are trying to achieve.  The first one is descriptive analysis. It helps summarize and report what has happened. So often using averages, totals, or percentages. For example, retailers use descriptive analysis to understand things like what was the average customer spend last quarter? How did store foot traffic trend across months?  The second one is diagnostic analysis. Diagnostic analysis digs deeper into why something happened. For example, hospitals use this type of analysis to find out, for example, why a certain department has higher patient readmission rates. Was it due to staffing, post-treatment care, or patient demographics?  The third one is predictive analysis. Predictive analysis looks forward, trying to forecast future outcomes based on historical patterns. For example, energy companies predict future electricity demand, so they can better manage resources and avoid shortages. And the last one is prescriptive analysis. So it does not just predict. It recommends specific actions to take.  So logistics and supply chain companies use prescriptive analytics to suggest the most efficient delivery routes or warehouse stocking strategies based on traffic patterns, order volume, and delivery deadlines.   09:42 Lois: So really, we’re using data science to solve everyday problems. Can you walk us through some practical examples of how it’s being applied?  Himanshu: The first one is predictive maintenance. It is done in manufacturing a lot. A factory collects real time sensor data from machines. Data scientists first clean and organize this massive data stream, explore patterns of past failures, and design predictive models.  The goal is not just to predict breakdowns but to optimize maintenance schedules, reducing downtime and saving milli

In this episode, hosts Lois Houston and Nikita Abraham, together with Senior Cloud Engineer Nick Commisso, break down the basics of artificial intelligence (AI). They discuss the differences between Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI), and explore the concepts of machine learning, deep learning, and generative AI. Nick also shares examples of how AI is used in everyday life, from navigation apps to spam filters, and explains how AI can help businesses cut costs and boost revenue.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. I’m so excited about this one because we’re going to dive into the world of artificial intelligence, speaking to many experts in the field. Nikita: If you've been listening to us for a while, you probably know we’ve covered AI from a bunch of different angles. But this time, we’re dialing it all the way back to basics. We wanted to create something for the absolute beginner, so no jargon, no assumptions, just simple conversations that anyone can follow. 01:08 Lois: That’s right, Niki. You don’t need to have a technical background or prior experience with AI to get the most out of these episodes. In our upcoming conversations, we’ll break down the basics of AI, explore how it's shaping the world around us, and understand its impact on your business. Nikita: The idea is to give you a practical understanding of AI that you can use in your work, especially if you’re in sales, marketing, operations, HR, or even customer service.  01:37 Lois: Today, we’ll talk about the basics of AI with Senior Cloud Engineer Nick Commisso. Hi Nick! Welcome back to the podcast. Can you tell us about human intelligence and how it relates to artificial intelligence? And within AI, I know we have Artificial General Intelligence, or AGI, and Artificial Narrow Intelligence, or ANI. What’s the difference between the two? Nick: Human intelligence is the intellectual capability of humans that allow us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using language and understand non-verbal cues, such as facial expressions, tone variation, body language. We can handle objections and situations in real time, even in a complex setting. We can plan for short and long-term situations or projects. And we can create music, art, or invent something new or have original ideas. If machines can replicate a wide range of human cognitive abilities, such as learning, reasoning, or problem solving, we call it artificial general intelligence.  Now, AGI is hypothetical for now, but when we apply AI to solve problems with specific, narrow objectives, we call it artificial narrow intelligence, or ANI. AGI is a hypothetical AI that thinks like a human. It represents the ultimate goal of artificial intelligence, which is a system capable of chatting, learning, and even arguing like us. If AGI existed, it would take the form like a robot doctor that accurately diagnoses and comforts patients, or an AI teacher that customizes lessons in real time based on each student's mood, pace, and learning style, or an AI therapist that comprehends complex emotions and provides empathetic, personalized support. ANI, on the other hand, focuses on doing one thing really well. It's designed to perform specific tasks by recognizing patterns and following rules, but it doesn't truly understand or think beyond its narrow scope. Think of ANI as a specialist. Your phone's face ID can recognize you instantly, but it can't carry on a conversation. Google Maps finds the best route, but it can't write you a poem. And spam filters catch junk mail, but it can't make you coffee. So, most of the AI you interact with today is ANI. It's smart, efficient, and practical, but limited to specific functions without general reasoning or creativity. 04:22 Nikita: Ok then what about Generative AI?  Nick: Generative AI is a type of AI that can produce content such as audio, text, code, video, and images. ChatGPT can write essays, but it can't fact check itself. DALL-E creates art, but it doesn't actually know if it's good. Or AI song covers can create deepfakes like Drake singing "Baby Shark."  04:47 Lois: Why should I care about AI? Why is it important? Nick: AI is already part of your everyday life, often working quietly in the background. ANI powers things like navigation apps, voice assistants, and spam filters. Generative AI helps create everything from custom playlists to smart writing tools. And while AGI isn't here yet, it's shaping ideas about what the future might look like. Now, AI is not just a buzzword, it's a tool that's changing how we live, work, and interact with the world. So, whether you're using it or learning about it or just curious, it's worth knowing what's behind the tech that's becoming part of everyday life.  05:32 Lois: Nick, whenever people talk about AI, they also throw around terms like machine learning and deep learning. What are they and how do they relate to AI? Nick: As we shared earlier, AI is the ability of machines to imitate human intelligence. And Machine Learning, or ML, is a subset of AI where the algorithms are used to learn from past data and predict outcomes on new data or to identify trends from the past. Deep Learning, or DL, is a subset of machine learning that uses neural networks to learn patterns from complex data and make predictions or classifications. And Generative AI, or GenAI, on the other hand, is a specific application of DL focused on creating new content, such as text, images, and audio, by learning the underlying structure of the training data.  06:24 Nikita: AI is often associated with key domains like language, speech, and vision, right? So, could you walk us through some of the specific tasks or applications within each of these areas? Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, extracting key phrases, and so on.  06:54 Lois: Ok, I get you. That’s like translating text, where you can use a text translation tool, type your text in the box, choose your source and target language, and then click Translate. That would be an example of a text-related AI task. What about generative AI language tasks? Nick: These are generative, which means the output text is generated by the model. Some examples are creating text, like stories or poems, summarizing texts, and answering questions, and so on. 07:25 Nikita: What about speech and vision? Nick: Speech-related AI tasks can be audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech to text conversion, speaker recognition, or voice conversion, and so on. Generative AI tasks are generative, i.e., the output audio is generated by the model (for example, music composition or speech synthesis). Vision-related AI tasks can be image related or generative AI. Image-related AI tasks use an image as the input, and the output depends on the task. Some examples are classifying images or identifying objects in an image. Facial recognition is one of the most popular image-related tasks that's often used for surveillance and tracking people in real time. It's used in a lot of different fields, like security and biometrics, law enforcement, entertainment, and social media. For generative AI tasks, the output image is generated by the model. For example, creating an image from a textual description or generating images of specific style or high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of objects, such as machine, buildings, medications, people and landscapes, and so much more. 08:58 Lois: This is so fascinating. So, now we know what AI is capable of. But Nick, what is AI good at? Nick: AI frees you to focus on creativity and more challenging parts of your work. Now, AI isn't magic. It's just very good at certain tasks. It handles work that's repetitive, time consuming, or too complex for humans, like processing data or spotting patterns in large data sets.  AI can take over routine tasks that are essential but monotonous. Examples include entering data into spreadsheets, processing invoices, or even scheduling meetings, freeing up time for more meaningful work. AI can support professionals by extending their abilities. Now, this includes tools like AI-assisted coding for developers, real-time language translation for travelers or global teams, and advanced image analysis to help doctors interpret medical scans much more accurately. 10:00 Nikita: And what would you say is AI's sweet spot? Nick: That would be tasks that are both doable and valuable. A few examples of tasks that are feasible technically and have business value are things like predicting equipment failure. This saves downtime and the loss of bu

In this episode, hosts Lois Houston and Nikita Abraham welcome back Cloud Delivery Lead Sarah Mahalik for a detailed tour of the four pillars of Oracle Fusion Cloud Applications: ERP, HCM, SCM, and CX.   Discover how Oracle weaves AI, analytics, and automation into every layer of enterprise operations. Plus, learn how Oracle Modern Best Practice is redefining digital workflows.   Oracle Fusion Cloud Applications: Process Essentials https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-hcm/146870 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundations-enterprise-resource-planning-erp/146928/241047 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-scm/146938 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-cx/146972   Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript:   00:00   Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!   00:25   Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and joining me is Nikita Abraham, Team Lead: Editorial Services.     Nikita: Hi everyone! Last week, we spoke about Oracle Cloud Apps and the Redwood design system. Today, we’ll take a closer look at the four key pillars of Oracle Cloud Apps.    Lois: And we’re so excited to have Sarah Mahalik back with us. Sarah is a Cloud Delivery Lead here at Oracle. Hi Sarah! In the last episode, we briefly spoke about the various Oracle Cloud Apps offerings and their capabilities. For anyone who missed that episode, can you give us a quick introduction?   01:06   Sarah: Oracle Cloud Applications is an incredibly broad suite that covers many of the most important business functions, from Human Capital Management, Supply Chain Management, to Enterprise Resource Planning and Customer Experience. The products in the Oracle Fusion Cloud Applications suite are organized by functional groups or pillars. All of these applications sit on Oracle Cloud Infrastructure, a foundation built from scratch to support mission-critical applications.    Oracle Fusion Applications deliver a single source of truth, enabling quick responses to disruptions and market opportunities. With unified data and consistent business rules, teams can build streamlined end-to-end processes, access real time analytics, and make faster data-driven decisions for improved outcomes.   01:52   Nikita: Ok, let’s actually get into each of these areas. I think we can start with Human Capital Management.   Sarah: Oracle Human Capital Management is an end-to-end solution that allows you to manage all aspects of people data from hire to retire. It all starts with recruiting, or requisitions are used to advertise vacant positions, and candidates are managed through the hiring process. After recruitment, successful candidates are transferred to the human resources module.   You can configure the organization structure to mirror that of your business. And this allows for easy reorganization whenever the structure changes. People data is a staple element of HCM. Therefore, as part of this product, an HR specialist can manage everything about the employee life cycle, including promotions, transfers, general assignment changes, and terminations.   A robust self-service offering allows employees and managers to take ownership and responsibility for the data pertaining to themselves and their teams. By removing the burden of simple data processing from the HR specialists, it not only eases the pressure on the HR department but allows them to concentrate on more specialized tasks.   03:00   Lois: And how are the core products of HCM categorized?   Sarah: The core products of Human Capital Management are categorized into four main groupings according to their logical purpose. First up, we have our human resources. This grouping includes the elements for implementing and maintaining the enterprise and workforce structure and employee life cycle data. This is where you would configure the organization structure as well as manage an employee's data from the HR specialist point of view. In addition, modules such as benefits, work life, workforce modeling and planning, and advanced HCM controls also sit within this category.   This brings us to talent management. This category is one of the largest because it includes recruiting, learning, goals and performance management, career development, succession planning, talent reviews, and compensation. In addition to that, dynamic skills and opportunity marketplace are also included in this grouping. Within workforce management, you'll find absence management and time and labor.   These naturally sit together because most organizations that implement both configure it so that an employee can enter both work time and absences on a time card, instead of having to visit two different entry points. You'll also find workforce health and safety here. And finally, payroll. All aspects of payroll are included here, whether you're simply using global payroll or localizations, such as UK, Canada, and Mexico.   It also encompasses payroll interface for those organizations that run their payroll from another system, and just need to extract and migrate the relevant data from Fusion HCM Cloud. When talking about HCM systems, we cannot forget the employee self-service aspect of the product. For this, there's an employee experience module called Oracle Me. Here you'll find options, such as HCM communicate, touchpoints, journeys, HR help desk, and Oracle digital assistant.   All of these combined enable an employee to take control and ownership of their own data, and use the many self-help options to get the information they need quickly and efficiently. In order to control how the system behaves and how users interact with it and perform the various processes, there are configuration options. These options allow organizations to define such things as the user experience, workflows, and approval policies based on their business requirements. And to meet the constant need for reporting, there's analytics, planning, and data modeling.   And in addition to all of that, you can use configuration options, such as extensibility, integration, or import and extracts, security, and adaptive intelligence to help enhance the system and have it working and looking the way you need. Of course, much of these latter configuration items are not exclusive to HCM but are available for the Oracle Fusion Cloud as a whole.   05:47   Lois: That’s great. Ok, let’s move on to Oracle Enterprise Resource Planning, or ERP.   Sarah: This is a complete modern Cloud ERP suite that provides your teams with advanced capabilities, such as AI, to automate the manual processes that slow them down, analytics to react to market shifts in real time, and automatic updates to stay current and gain a competitive advantage.   Oracle Cloud ERP automates the entire Record to Report process and provides a common repository of information for global financial reporting and compliance. Within ERP, we have the broadest and deepest suite offering everything you need, from financials, project management, enterprise performance management, risk management and compliance, and analytics.    06:34   Nikita: Sarah, could you break down the different modules within ERP?   Sarah: First, we have Financials, which is a global financial platform that connects and automates your financial management processes, including payables, receivables, fixed assets, expenses, and reporting for a clear view into your total financial health. Oracle Project Management offers a single project cloud solution designed to help you gain a complete picture of your organization's project finances and operations. It's seamlessly integrated across the enterprise with the Oracle Fusion Cloud ERP, HCM, and SCM applications.   Oracle Fusion Cloud Enterprise Performance Management, or EPM, helps you model and plan across finance, HR, supply chain and sales, streamline the financial close process, and drive better decisions. Oracle Fusion Cloud Risk Management and Compliance is a security and audit solution that controls user access to your Oracle Cloud ERP financial data, monitors user activity, and makes it easier to meet compliance regulations through automation.   Oracle Risk Management Compliance uses AI and ML to strengthen financial controls to help prevent cash leaks, enforce audit, and protect against emerging risks, saving you hours of manual work. Oracle Analytics for Cloud ERP complements the embedded analytics in Cloud ERP to provide pre-packaged use cases, predictive analysis, and KPIs based on variance analysis and historical trends.    08:06   Lois: And what about Supply Chain Management?   Sarah: Oracle Supply Chain Management empowers organizations to plan, source, make, deliver, and service goods with agility and resilience. It offers a solution that integrates advanced capabilities, such as AI/ML and blockchain, to optimize the supply chain life cycle from start to finish.   08:31   Adopting a multicloud strategy is a big step towards future-proofing your business and we’re here to help you navigate this complex landscape. With our suite of courses, you'll gain insights into network connectivity, security protocols, and the considerations of working across different cloud platforms. Start your journey today to multicloud today by visiting myle

Join hosts Lois Houston and Nikita Abraham, along with Cloud Delivery Lead Sarah Mahalik, as they unpack the core pillars of Oracle Fusion Cloud Applications—ERP, HCM, SCM, and CX.   Learn how Oracle’s SaaS model, Redwood UX, and built-in AI are reshaping business productivity, adaptability, and user experience. From quarterly updates to advanced AI agents, discover how Oracle delivers agility, lower costs, and smarter decision-making across departments.   Oracle Fusion Cloud Applications: Process Essentials https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-hcm/146870 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundations-enterprise-resource-planning-erp/146928/241047 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-scm/146938 https://mylearn.oracle.com/ou/course/oracle-fusion-cloud-applications-foundation-cx/146972   Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs.  Lois: Hi everyone! In our last two episodes, we explored the Oracle Cloud Success Navigator platform. This week and next, we’re diving into Oracle Fusion Cloud Applications with Sarah Mahalik, a Cloud Delivery Lead here at Oracle. We’ll ask Sarah about Oracle’s cloud apps suite, the Redwood design system, and also look at some of Oracle’s AI capabilities.  01:02 Nikita: Yeah, let’s jump right in. Hi Sarah! How does Oracle approach the SaaS model? Sarah: Oracle's Cloud Applications suite is a complete enterprise cloud designed to modernize your business. Our cloud suite of SaaS applications, which includes Enterprise Resource Planning, or ERP, Supply Chain Management, or SCM, Human Capital Management, or HCM, and Customer Experience, or CX, brings consistent processes and a single source of truth across the most important business functions.  At Oracle, we own all of the technology stacks that power our suite of cloud applications. Oracle Cloud Applications are built on Oracle Cloud Infrastructure and ensure the performance, resiliency, and security that enterprises need. Your business no longer needs to worry about maintaining a data center, hardware, operating systems, database, network, or all of the security. With deep integrations, a common data model, and a unified user interface, these applications help improve customer engagement, increase agility, and accelerate response to change. Oracle's Cloud Applications are updated quarterly with new features and improvements. These updates are based on our deep understanding of customer's functional needs, as well as modern technologies such as artificial intelligence, machine learning, blockchain, and digital assistants. Expectations for user experience only go up. Oracle's Redwood User Experience methodology ensures those expectations are matched and exceeded by including powerful and predictive search, a look and feel that actually helps users see what they need to in the order they need to see it, and by providing conversational and micro-interactions. Oracle, as a SaaS provider, puts the customer first by having enough dedicated resources to ensure zero downtime and increasing the speed of implementation by eliminating much of the hardware and software setup activity. 02:59 Nikita: What are the advantages of adopting Oracle Cloud Apps? Sarah: First off, Oracle provides automatic quarterly updates, and they're usable immediately. Customers can focus on leveraging the new functionality instead of spending cycles on installing it. There's much more accessibility because Oracle hosts the heavy part of the applications and customers access it via thin clients. The applications can be used from nearly anywhere and on a wide range of devices, including smartphones and tablets. Another great advantage is speed and agility. A lot of the benefits you see here result from Oracle's provider model. That means customers aren't spending time on customization, application testing, and report development. Instead, they work on the much lighter and faster tasks of configuration, validation, and leveraging embedded analytics. And finally, it's just better economics. Because of the pricing model, it is easy to compare an on-premises implementation cost. While upfront costs are almost always lower, overall operational costs and risk are usually lower as well. This translates to better total cost of ownership and improved overall economics and agility for your business. 04:10 Lois: Sarah, in your experience, why do customers love Oracle Cloud Apps? Sarah: At Oracle, we empower you with embedded AI that drives real breakthroughs in productivity and efficiency, helping you stay ahead of the curve. With the power of Oracle Cloud Infrastructure, you get the best of performance, security, and scalability, making it the perfect foundation for your business. Our modern user experience is intuitive and designed with your needs in mind, while our relentless innovation is focused on what truly matters to you. Above all, our commitment to your success is unwavering. We're here to support you every step of the way, ensuring you thrive and grow with Oracle. 04:49 Lois: Let’s talk about Oracle’s Redwood design system. What is it? And how does it enhance the user experience?  Sarah: Redwood is the name of Oracle's next-generation user experience. Redwood design system is a collection of prefabricated components, templates, and patterns to enable developers to quickly create very sophisticated and polished interactions that are upgrade safe. It provides a consumer grade-plus experience, where you have high-quality functionality that can be used across multiple devices. You have access to insightful data readily at your fingertips for quick access and decision making, with the option to personalize your application to create your own state of the art experience. Processes and entry time will now be more efficient and streamlined by having fewer clicks and faster downloads, which will lead to high productivity in areas that matter the most. The Redwood design is intelligent, meaning you have access to AI, where you will receive recommendations and guidance based on your preferences and business processes. It's also adaptable, allowing you to use the same tools to create new experiences by using the Business Rule Framework with modern UX components. Oracle's Redwood user experience will help you to be more productive, efficient, and engaged with a highly personalized experience. 06:11 Are you keen to stay ahead in today's fast-paced world? We’ve got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you’re always in the know, we offer New Features courses that give you an insider’s look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started.  06:37 Nikita: Welcome back! Sarah, you said the Redwood design system is adaptable. Can you elaborate on what you mean by that?  Sarah: In a nutshell, this means that developers can extend their applications using the same development platform that Oracle Cloud Applications are built on. Oracle Visual Builder Studio is a robust application development platform that enables users to rapidly create and extend web, mobile, and progressive web interfaces using a visual development environment. It streamlines application development and reduces coding, while also providing flexibility and support for popular build and testing frameworks. With Oracle Visual Builder Studio, users can build apps for the web, create progressive web apps, and develop on-device mobile apps. The tool also offers access to REST services and allows for planning and managing development processes, as well as managing the code lifecycle. Additionally, Oracle Visual Builder Studio provides hosting for apps along with easy publishing and version management. Changes made using Visual Builder Studio are called Application Extensions.  Visual Builder Studio Express Mode has two key components: Business Rules and Constants. Use Business Rules, which is the Redwood equivalent to Transaction Design Studio for responsive pages, to leverage delivered best practices or create your own rules based on various criteria, such as country and business unit. Make fields and regions required or optional, read-only or editable, and show or hide fields in regions, depending on specific criteria. Use the various delivered Constants to customize your Redwood pages to best fit your specific business needs, such as hide the evaluation panel and connections or reorder the columns in the person search result table. 08:23 Lois: Sarah, here’s a question that's probably on everyone's mind—what about AI for Fusion Applications? Sarah: Oracle integrates AI into Fusion Applications, enabling faster, better decision making and empowering your workforce. With both classic and generative AI embedded, customers can access AI-driven insights seamlessly within their everyday software environment. In HCM, AI helps to automate routine tasks. It's also used to attract and manage talent more efficiently by doing things like reducing the time to hire and performing automatic skill matching for job vacancies. It also uses some

Hosts Lois Houston and Nikita Abraham continue their discussion with Mitchell Flinn, VP of Program Management for the CSS Platform, by exploring how Oracle Cloud Success Navigator helps teams align faster, reduce risk, and drive value.   Learn how built-in quality benchmarks, modern best practices, and Starter Configuration tools accelerate cloud adoption, and explore ways to stay ahead with a mindset of continuous innovation.   Oracle Cloud Success Navigator Essentials: https://mylearn.oracle.com/ou/course/oracle-cloud-success-navigator-essentials/147489/242186 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University and with joining me today is Nikita Abraham, Team Lead of Editorial Services.  Nikita: Hi everyone! In our last episode, we gave you a broad overview of the Oracle Cloud Success Navigator platform—what it is, how it works, and its key features and benefits. Today, we’re continuing that discussion with Mitchell Flinn. Mitchell is VP of Program Management for Oracle Cloud Success Navigator, and in this episode, we’re going to ask him to walk us through some of the core components of the platform that we couldn’t get into last week. 01:04 Lois: Right, Niki. Hi Mitchell! You spoke a little about Cloud Quality Standards in our last episode. But how do they contribute or align with the vision of Oracle Cloud Success Navigator?  Mitchell: The vision for Navigator is to support customers throughout every phase of their cloud journey, providing timely advice to help improve outcomes to reduce cost and increase overall value. This model is driven through Oracle Cloud Quality Standards. These standards are intended to improve the transparency and collaboration between customer, partner, and Oracle members of a project. This is a project blueprint to include the ability for business and IT users to align on project coordination, expectations, and ultimately drive tighter alignment. Tracking key milestones and activities can help visualize and measure progress. You can build assessments and help answer questions so that at the right time, you have the right resources to make the right decisions for an organization. Cloud Quality Standards represent the key milestone dates and accomplishments along the journey. You can leverage these to increase project transparency, reduce risk, and increase the overall collaboration. Cloud Quality Standards are proactive list of must haves leveraged by customers, partners, and Oracle. They're a collection of knowledge and lessons learned from thousands of implementations globally. Cloud Quality Standards are partner agnostic and complimentary to all SI methodologies and tool sets. And they've been identified to address delivery issues before they happen and reduce the risk of implementations. 02:34 Lois: Ok, and a crucial component of Oracle Cloud Success Navigator is Oracle Modern Best Practice, or OMBP, right? Can you tell us more about what this is?  Mitchell: Oracle Modern Best Practices are based on distilled knowledge of our customers' needs gained from 10,000 successful delivery projects. They illustrate the business process components and their optimization to take advantage of the latest Oracle applications and technologies. Oracle Modern Best Practices comprise industry best practices and processes powered by Oracle technology. Engineered in Fusion Applications, OMBPs simplify and streamline workflows. They enable organizations to leverage modern, efficient, and scalable practices. As we align our assets with OMBPs, there's a stronger connection between global process owners and business process innovation within a customer's organization. 03:21 Nikita: And how do they help deliver end-to-end success for businesses?  Mitchell: An OMBP approach involves a digital business process, so evolving and adapting in real time to changing market dynamics. End-to-end across the organization, so we're breaking down silos and ensuring there's operational agility and a seamless collaboration between departments. We're leveraging emerging technologies, so utilizing AI, other cutting-edge technologies to automate routine tasks, enabling greater human creativity and unlocking new value and insights. And radically superior results, driving a significant improvement in measurable outcomes. OMBPs are dynamic, and when regularly updated, they meet evolving customer needs and technologies. They're trusted, tested, and validated by Oracle experts and publicly available and download on oracle.com. If you go to oracle.com and search modern best practice, you'll find more detailed introduction to Oracle Modern Best Practices. You'll also find Oracle Modern Best Practice business processes for domains such as ERP, EPM, Supply Chain, HCM, and Customer  Experience. We also have Oracle Modern Best Practices for specific industries. 04:25 Nikita: What are the key benefits of OMBP? Mitchell: Revolutionary new technologies are available for organizations and business leaders. You might wonder how existing business processes are optimized with old technology and how they can drive the best solution. With more emerging technologies reaching commercial availability, existing best practices become outdated. And to stay competitive, organizations need to continuously innovate and incorporate new technology within their best practices. In Oracle's definition of OMBPs, common business processes are considered historic input, but we also factor in what could be done with new technologies. And based on this approach, Oracle Modern Best Practices help us evolve with the organizational needs as market dynamics change, work end to end across organizations to eliminate department silos and ensure agility. It allows us to use technologies such as AI to automate the mundane and unlock human creativity for new value and insight. This allows us to incorporate next generation digital technologies to enable radically superior, measurable results. To achieve these, Oracle makes use of key differentiators such as analytics and AI and machine learning. Analytics are also known as business intelligence provides you with information in the form of pre-built dashboards, showing your key metrics in real time. Embedded analytic capabilities enable you to monitor business performance and make better decisions. 05:44 Lois: And what about AI and machine learning? Mitchell: These focus on building systems that learn or improve performance based on the data that they consume. Smart digital assistants, recommendation engines, predictive analytics, they're all used within AI and machine learning to help organizations automate operations and drive innovation, and ultimately make better decisions faster. 06:02 Nikita: Mitchell, let’s move on to the Starter Configuration. Can you explain what it is and how it helps during a cloud implementation? Mitchell: Starter Configuration is a predefined configuration of Oracle Cloud Applications aligned with the Oracle Modern Best Practices. It's very comprehensive and includes business processes in several domains, such as ERP, HCM, Supply Chain, EPM, and so on. It includes sample, master, and transactional data, and predetermined usernames, which aligns and tests based on the same use cases you saw in Oracle Modern Best Practices in Cloud Success Navigator. Customers can request deployment of a Starter Configuration into their test environment. Oracle will run an automated process for replicating the configuration, master data, transaction data, and predetermined usernames from Oracle to the Oracle Cloud Applications Test Environment of the customer's choice. For best user experience, customers can add a basic level of personalization, such as their customer name, limited number of employees, suppliers, customers, and a few other items. Starter Configuration’s delivered with predetermined step guides for comprehensive set of use cases. Using these, customers can relay the same use cases they've seen in Oracle Modern Best Practices and Success Navigator. In the Oracle Cloud Applications Test Environment Customer, we've been able to enable an in-app guidance using Oracle Guided Learning. This helps to make it easier for navigation through the business processes supported by the application. Oracle can deploy the Starter Configuration in days, not weeks or months, which means the implementation partners don't need to invest time and effort for the first configuration of an Oracle Cloud Application environment before they can even get the chance to show it to a customer. In turn, once Starter Configuration is deployed, it's ready to be used for solution familiarization and design activities. Using Starter Configuration of Oracle Cloud Applications early in the cloud journey will offer several benefits to customers. 08:00 Lois: What are these benefits? Mitchell: The first, it helps to cut down on environment configuration time from several weeks or months to potentially just days. Next, implementation partners can engage stakeholders early, and get them familiar with Oracle Cloud Applications, especially those that maybe have never participated in the sales cycle. Because customer stakeholders actually see what Oracle Cloud solutions might look like in the future, it becomes easier to take design decisions. Starte

In this episode of the Oracle University Podcast, Lois Houston and Nikita Abraham are joined by Mitchell Flinn, VP of Program Management for the CSS Platform, to explore Oracle Cloud Success Navigator.   This interactive platform is designed to help customers optimize their cloud journey, offering best practices, AI tools, and personalized guidance from implementation to innovation. Don’t miss this insider look at maximizing your Oracle Cloud investment!   Oracle Cloud Success Navigator Essentials: https://mylearn.oracle.com/ou/course/oracle-cloud-success-navigator-essentials/147489/242186 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and joining me is my co-host Lois Houston, Director of Innovation Programs.  Lois: Hi everyone! Today is the first of a two-part special on Oracle Cloud Success Navigator. This is a tool that provides you with a clear path to cloud transformation and helps you get the most out of your cloud investment. 00:52 Nikita: And to tell us more about this, we have Mitchell Flinn joining us today. Mitchell is VP of Program Management for Oracle Cloud Success Navigator. In this episode, we’ll ask Mitchell about the ins and outs of this powerful platform, its benefits, key features, and the role it plays in streamlining cloud journeys. Lois: Yeah. Hi Mitchell! What is Oracle's approach to cloud technology and customer success, and how does the Cloud Success Navigator support this philosophy? 01:22 Mitchell: Oracle has an amazing amount of industry-leading enterprise cloud technologies across our entire portfolio. All of this is at your disposal. That, coupled with the sole focus of your success, forms the crux of the company's transformational journey. In other words, we put your success at the heart of everything we do. For each organization, the path to achieve maximum value from our technology is unique. Success Navigator reflects our emphasis on being there with you throughout the entire journey to steer you to success.  01:53 Nikita: Ok, what about from a business’s viewpoint? Why would they need the Navigator? Mitchell: Businesses across every industry are moving mission-critical applications to the cloud. However, business leaders understand that there's no one-size-fits-all model for cloud development and deployment. Some fundamentals for success are your need to ensure new technologies are seamlessly integrated into day-to-day operations and continually optimize to align with evolving business requirements. You must ensure stakeholder visibility through the journey with updates at every stage. Building system efficiencies into other key tasks, which has to be done at the forefront when considering your cloud transformation. You also need to quickly identify risks and address them during the implementation process and beyond. Beyond the technical execution, cloud deployments also require significant process and organizational changes to ensure that adoption is aligned with business goals and delivers tangible benefits. Moreover, the training process for new features after cloud adoption can be an organization wide initiative that needs special attention. These requirements or more can be addressed through Oracle Cloud Success Navigator, which is a new interactive digital platform to guide you through all stages of your cloud journey. 03:09 Lois: Mitchell, how does Cloud Success Navigator platform enhance the user experience? How does it support customers at different stages of their cloud journey? Mitchell: Platform is included for free for all cloud application customers. And core to Success Navigator is the goal of increasing transparency among customers, partners in the Oracle team, from project kickoff through quarterly releases. Included in the platform are implementation best practices, Oracle Modern Best Practices focused on solutions provided by our applications, and guidance on living within the cloud.   Success Navigator supports you for every stage of your Oracle journey. You can first get your bearings and understand what's possible with your cloud solution using preconfigured starter environments to support your design decisions. It helps you chart a proven course by providing access to Oracle expertise and Oracle Modern Best Practices, so you can use cloud quality standards to guide your implementation approach. You can find value from quarterly releases using AI assistants and preview environments to experience and adopt latest features that matter to you. And you can blaze new trails by building your own cloud roadmap based on your organization's goals, keeping you focused on the capabilities you need for day-to-day and the road ahead. 04:24 Nikita: How does the Navigator cater to the needs of all the different customers? Mitchell: For customers just getting started with Oracle implementations, Navigator provides a framework with success criteria for each stakeholder involved in the implementation, and provides recommended milestones and checklists to keep everyone on track. For existing customers and experienced cloud customers thriving in the cloud, it provides contextually relevant insights based on your cloud landscape. It prepares you for quarterly releases and preview environments, and enables the use of AI and optimization within your cloud investment. For our partners, it allows Oracle to work in a collaborative way to really team up for our customers. Navigator gives transparency to all stakeholders and helps determine what success criteria we should be thinking about at each milestone phase of the journey. And it also helps customers and partners get more out of their Oracle investment through a seamless process. 05:20 Lois: Right. Mitchell, can you elaborate on the use cases of the platform? How does it address challenges and requirements during cloud implementations? Mitchell: We can create transparency and alignment between you, your partner, and the Oracle team using shared view of progress measured through standard criteria. We can incorporate recommended key milestones and activities to help you visualize and measure your progress. And we can use built-in assessments, remove risk, and ask the right questions at the right time to make the right implementation decisions for your organization. Additionally, we can use Starter Configuration, which allows you to experience the latest capabilities and leading practices to enrich design decisions for your organization with Starter Configuration. This can activate Starter Configuration early in your journey to understand what delivered capability can do for you. It allows you to evaluate modern best practices to determine how your design process can work in the future. And it empowers and educates your organization by interacting with real capability, using real processes to make the right decisions for your cloud implementation. You're able to access new features in Fusion updates. You can do this to learn what you need to know about new features in a one-stop shop and connect your company in a compelling capacity. You can find, familiarize, and prioritize new and existing features in one place. And you can experience new features in hands-on preview environments available with you with each quarterly release. You can explore new theme-based approaches using adoption centers for AI and Redwood to understand where to start and how to get there. And you can understand innovation opportunities based on business processes, data insights, and industry benchmarks. 07:01 Nikita: Now that we’ve covered the basics, can you walk us through some of the key features of the platform? Let’s start with the Home page. Mitchell: This is the starting point of the customer journey and the central location for everything Navigator has to offer, including best practice content. You'll find content focused on implementation phase, the innovation phase, and administrative elements like the team structure, program and projects, and other relevant tips and tricks. Cloud Quality Standards provides learning content and checklists for successful business transformation. This helps support the effective adoption and adherence to Cloud Quality Standards and enables individuals to leverage AI and predictive insights. The feature Sunburst allows capability for features to be reviewed in an interactive graphic, illustrating new features by pillar, other attributes, which enable customers to review features curated to identify and adopt new features that meet their needs. It helps you understand recommended features across your application base based off of a production profile, covering mandatory adoption requirements, efficiency gains, innovation capabilities like AI and Redwood to drive business change. Next is the Adoption Center, which addresses the need of our existing and implementing customers. It covers the concept of how Redwood is an imperative for our application customers, what it means, and how, and when we could translate some of the requirements to a business user or an IT user. Roadmap is an opportunity for the team to evaluate which features are most interesting at any given moment, the items that they would like to adopt next, and save features or items that they might require later. 08:36 Lois: That’s great. Mitchell, I know

In the final episode of this series on Oracle GoldenGate 23ai, Lois Houston and Nikita Abraham welcome back Nick Wagner, Senior Director of Product Management for GoldenGate, to discuss how parameters shape data replication. This episode covers parameter files, data selection, filtering, and transformation, providing essential insights for managing GoldenGate deployments.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------------------- Podcast Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! This is the last episode in our Oracle GoldenGate 23ai series. Previously, we looked at how you can manage Extract Trails and Files. If you missed that episode, do go back and give it a listen.  00:50 Lois: Today, Nick Wagner, Senior Director of Product Management for GoldenGate, is back on the podcast to tell us about parameters, data selection, filtering, and transformation. These are key components of GoldenGate because they allow us to control what data is replicated, how it's transformed, and where it's sent. Hi Nick! Thanks for joining us again. So, what are the different types of parameter files? Nick: We have a GLOBALS parameter file and your runtime parameter files. The global one is going to affect all processes within a deployment. It's going to be things like where's your checkpoint table located in name, things like the heartbeat table. You want to have a single one of these across your entire deployment, so it makes sense to keep it within a single file. We also have runtime parameter files. This are going to be associated with a specific extract or replicat process. These files are located in your OGG_ETC_HOME/conf/ogg. The GLOBALS file is just simply named GLOBALS and all capitals, and your parameter file names for the processes themselves are named with the process.prm. So if my extract process is EXT demo, my parameter file name will be extdemo.prm. When you make changes to parameter files, they don't take effect until the process is restarted. So in the case of a GLOBALS parameter file, you need to restart the administration service. And in a runtime parameter file, you need to restart that specific process before any changes will take effect. We also have what we call a managed process setting profile. And this allows you to set up auto restart profiles for each process. And the GoldenGate Gate classic architecture, this was contained within the GLOBALS parameter file and handled by the manager. And microservices is a little bit different, it's handled by the service manager itself. But now we actually set up profiles. 02:41 Nikita: Ok, so what can you tell us about the extract parameter file specifically?  Nick: There's a couple things within the extract parameter file is common use. First, we want to tell what the group name is. So in this case, it would be our extract name. We need to put in information on where the extract process is going to be writing the data it captures to and that would be our trail files, and extract process can write to one or more trail files. We also want to list out the list of tables and schemas that we're going to be capturing, as well as any kind of DDL changes. If we're doing an initial load, we want to set up the SQL predicate to determine which tables are being captured and put a WHERE clause on those to speed up performance. We can also do filtering within the extract process as well. So we write just the information that we need to the trail file. 03:27 Nikita: And what are the common parameters within an extract process? Nick: There are a couple of common parameters within your extract process. We have table to list out the list of tables that GoldenGate is going to be capturing from. These can be wildcarded. So I can simply do table.star and GoldenGate will capture all the tables in that database. I can also do schema.star and it will capture all the tables within a schema. We have our EXTTRAIL command, which tells GoldenGate which trail to write to. If I want to filter out certain rows and columns, I can use the filter cols and cols except parameter. GoldenGate can also capture sequence changes. So we would use the sequence parameter. And then we can also set some high-level database options for GoldenGate that affect all the tables and that's configured using the tranlog options parameter.  04:14 Lois: Nick, can you talk a bit about the different types of tranlogoptions settings? How can they be used to control what the extract process does? Nick: So one of the first ones is ExcludeTag. So GoldenGate has the ability to exclude tagged transactions. Within the database itself, you can actually specify a transaction to be tagged using a DBMS set tag option. GoldenGate replicat also sets its transactions with a tag so that the GoldenGate process knows which transactions were done by the replicat and it can exclude them automatically. You can do exclude tag with a plus. That simply means to exclude any transaction that's been tagged with any value. You can also exclude specific tags.  Another good option for TranLogOptions is enable procedural replication. This allows GoldenGate to actually capture and replicate database procedure calls, and this would be things like DBMS AQ, NQ operations, or DQ operations. So if you're using Oracle advanced queuing and you need GoldenGate to replicate those changes, it can.  Another valuable tranlogoption setting is enable auto capture. Within the Oracle Database, you can actually set ALTER TABLE command that says ALTER TABLE, enable logical replication. Or when you create a table, you can actually do CREATE TABLE statement and at the end use the enable logical replication option for that CREATE TABLE statement. And this tells GoldenGate to automatically capture that table. One of the nice features about this is that I don't need to specify that table and my parameter file, and it'll automatically enable supplemental logging on that table for me using scheduling columns. So it makes it very easy to set up replication between Oracle databases.  06:01 Nikita: Can you tell us about replicat parameters, Nick? Nick: Within a replicat, we'll have the group name, some common other parameters that we'll use is a mapping parameter that allows us to map the source to target table relationships. We can do transformation within the replicat, as well as error handling and controlling group operations to improve performance. Some common replicat parameters include the replicat parameter itself, which tells us what the name of that replicat is. We have our map statement, which allows us to map a source object to a target object. We have things like rep error that control how to handle errors. Insert all records allows us to change and convert, update, and delete operations into inserts. We can do things like compare calls, which helps with active-active replication in determining which columns are used in the GoldenGate WHERE clause. We also have the ability to use macros and column mapping to do additional transformation and make the parameter file look elegant. 07:07 AI is being used in nearly every industry…healthcare, manufacturing, retail, customer service, transportation, agriculture, you name it! And it’s only going to get more prevalent and transformational in the future. It’s no wonder that AI skills are the most sought-after by employers. If you’re ready to dive in to AI, check out the OCI AI Foundations training and certification that’s available for free! It’s the perfect starting point to build your AI knowledge. So, get going! Head on over to mylearn.oracle.com to find out more. 07:47 Nikita: Welcome back! Let’s move on to some of the most interesting topics within GoldenGate… data mapping, selection, and transformation. As I understand, users can do pretty cool things with GoldenGate. So Nick, let’s start with how GoldenGate can manipulate, change, and map data between two different databases. Nick: The map statement within a Replicat parameter allows you to provide specifications on how you're going to map source and target objects. You can also use a map and an extract, but it's pretty rare. And that would be used if you needed to write the object name. Inside the trail files is a different name than the actual object name that you're capturing from. GoldenGate can also do different data selection, mapping, and manipulation, and this is all controlled within the Extract and Replicat parameter files. In the classic architecture of GoldenGate, you could do a rudimentary level of transformation and filtering within the extract pump. Now, the distribution service is only allowing you to do filtering. Any transformation that you had within the pump would need to be moved to the Extract or the Replicat process.  The other thing that you can do within GoldenGate is select and filter data based on different levels and conditions. So within your parameter clause, you have your Table and Map statement. That's the core of everything. You have your filtering. You have COLS and COLSEXCEPT, which allow you to determine which columns you're going to include or exclude from replication. The Table and Map stat

In this episode of the Oracle University Podcast, Lois Houston and Nikita Abraham explore the intricacies of trail files in Oracle GoldenGate 23ai with Nick Wagner, Senior Director of Product Management.   They delve into how trail files store committed operations, preserving the order of transactions and capturing essential metadata. Nick explains that trail files are self-describing, containing database and table definition records, making them easier to work with. The episode also covers trail file management, including the purge trail task and the ability to download trail files directly from the web UI, providing flexibility in various deployment scenarios.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Podcast Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome back to another episode of the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and I’m joined by Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we discussed the Replicat process. That was a good introduction, and you should give it a listen if you’re interested in the fundamentals of GoldenGate 23ai.  00:49 Nikita: Nick Wagner, Senior Director of Product Management for Oracle GoldenGate, is back with us today to talk about how to manage Extract Trails and Files. Hi Nick, it’s a pleasure to have you with us. So, we’ve spoken about trail files in our earlier episodes. But can you tell us about the kind of information that’s actually stored in these files?  Nick: The trail files contain committed operations only. In an Oracle environment, the extract process is actually able to understand and read both committed and uncommitted transactions. It holds the uncommitted activity and the cache manager associated settings. As a transaction is committed, it's then flushing that information to the trail file. All this information in the transaction is preserved, so we have not only the transaction itself, but the order of the operations within that transaction. All the changed columns, including the primary key and any scheduling columns are also captured, and this is controlled by the log or sub calls parameter and other parameters within the extract process. The data captured depends on settings in the extract file and you can include additional information, including tokens. The trail files also contain metadata information, where the trail files are what we call self-describing, which means that as we start reading in new objects, we start writing the definition of those objects into the trail file themselves. 02:11 Lois: Nick, what does the structure of a trail file look like? Nick: The trail files have a header information, which simply keeps information about what version of trail file this is, where GoldenGate is handling it, information about that trail file itself. You'll also have three different types of records. You'll have a data record, which contains the actual before and after images, the table update statement, the type of operations. You have a database definition record, which includes information about the database that GoldenGate is capturing from, and then you'll also have a table definition record. As GoldenGate starts up and creates a trail file for the first time, it's always going to write the trail file header and associated database definition record, and then it's going to start reading data out of the source database. As it encounters a new table for the first time in that trail file, it's going to write the metadata for that object as well. This makes it very easy. This means that within a single trail file, any data records I have in there, that trail file also contains the associated table definition record for that table. 03:20 Nikita: Let’s talk about compatibility between different versions of GoldenGate. How do the trail files fit into that? Nick: The GoldenGate trail files themselves have information built into them to help understand what they're compatible with as far as GoldenGate releases. If I'm replicating from a new version of GoldenGate to an older version of GoldenGate, I can set the format release value to tell the extract process to write these trail files in an older version. In this case, I can simply say format release 19 and it'll write the trail files in the 19C version. If you're going from an older version to a newer version of GoldenGate, it's automatically able to process the old version trail file structure without having to change anything.  04:02 Nikita: Now, GoldenGate is constantly generating these trail files as it runs. So, how do we manage them over time. What’s the cleanup process like? Nick: Within the GoldenGate microservices architecture, the web UI has a way to manage your trail files and clean them up. So there's a purge trail task that allows you to go in and set up rules on how long to keep the trail files around for before they're purged. We have customers that want to reposition extract and so you'll want to make sure that you keep trail files around long enough so that you can handle any reposition that you intend to do. Trail files will always be kept around even past their purge rules if they're still needed for GoldenGate recovery. Also new to GoldenGate 23ai is the ability to download trail files directly from the web UI. This is extremely helpful if you're using OCI GoldenGate or you don't have OS access on the machine where GoldenGate is running. 04:56 Lois: What if we want to look inside these trail files and actually see the individual records? How can we examine them directly? Nick: Well, that can be seen using a tool called Logdump. Logdumps utility, that's installed in your ogghome/bindirectory. It has online help as well as full documentation. 05:14 Lois: And how do you use Logdump? Nick: So to use Logdump, the first thing you'll do is launch the service and then you'll open a trail file. You would specify the full path of the trail file along with the path name and the sequence number of that trail file. Once you've set it up, you'll position into that location within that trail file. Normally people position at record 0 and then they'll do a next, which allows them to get the next information. There's a couple other commands in there, such as POS, which allows you to set the position, scan for header, allows you to scan to the next record if you position within the middle of a record. So, when you first run Logdump, it's not going to have very much information available for you. So, you'll want to turn on a couple of settings. You'll want to enable File Header, GHDR, and Detail to be able to see more information about what's going on within that record within the trail. Logdump also has the ability to show you the actual ASCII values as opposed to the text value. This is very useful for dealing with multibyte data as well as unprintable characters. You can also specify the length of the record to show for each Logdump record. And this is in the reclen parameter, 280 is a rough number and it will usually show about enough that'll fit on a single page. 06:40 Join the Oracle University Learning Community and tap into a vibrant network of over 1 million members, including Oracle experts and fellow learners. This dynamic community is the perfect place to grow your skills, connect with likeminded learners, and celebrate your successes. As a MyLearn subscriber, you have access to engage with your fellow learners and participate in activities in the community. Visit community.oracle.com/ou to check things out today! 07:12 Nikita: Welcome back! Nick, earlier you mentioned data records in trail files. What kind of information do these records contain? Nick: When we start looking at data records within the trail file, we're going to see a little bit different format. It's going to give us information about what type of operation this was, the before, after indicator, is this an after image or a before image? It's going to give us the time information. It's going to tell us what table this record was on and the values within that record. We can also count the number of records in a trail using the count option that tells us how many records in the trail, the average size, and then the operation type breakdown. We can also get some additional details on that count, including having it broken out by table and operation within those tables. This is really useful if you're trying to track down a missing record or an out of sync condition and you want to make sure that GoldenGate is appropriately capturing all the changes. We can also use an option within Logdump called scan for metadata. The shorthand for this command is sfmd, it allows you to scan for something like a database definition record.  You may have multiple database definition records versions within the same trail file. It tells us what type of database this was, the character set, which is important because this information is used by the replica when it goes to apply changes into the target database. We can also scan for metadata to get table definition records. The data types are numeric values that are associated with an internal GoldenGate data type. 08:43 Lois: Thank you, Nick, for your insights. There’s a lot more you can find in the Oracle GoldenGate 23ai: Fundamentals c

In this episode, Lois Houston and Nikita Abraham, along with Nick Wagner, Senior Director of Product Management, dive into the Replicat process in Oracle GoldenGate 23ai.   They discuss how Replicat applies changes to the target database, highlighting the different types: Classic, Coordinated, and Parallel Replicat.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to another episode of the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! If you’ve been listening to us these last few weeks, you’ll know we’ve been discussing the fundamentals of GoldenGate 23ai. Today is going to be all about the Replicat process. Again, this is something we’ve discussed briefly in earlier episodes, but just to recap, the Replicat process applies changes from the source database to the target database. It's responsible for reading trail files and applying the changes to the target system. 01:04 Lois: That’s right, Niki. And we’ll be chatting with Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Thanks for joining us again today. Let’s get straight into it. Can you give us an overview of the Replicat process? Nick: One thing that's very important is the Replicat is extremely chatty with that target database. So it's going to be going in and trying to make lots of little transactions on that system. The Replicat process only issues single row DML. So if you can imagine a source database that's generating hundreds of thousands of changes per second, we're going to have to have a Replicat process that can do 100,000 changes per second on that target site. That means that it's going to have to send a lot of little one record commands. And so we've got a lot of ways to optimize that. But in all situations you're really going to want very, very low ping time between that Replicat process and that target database. This often means that if you're going to be running GoldenGate in a cloud, you're going to want the Cloud GoldenGate environment to be running in that target data center, wherever that target database is. 02:06 Lois: What are the key characteristics of the process, Nick? Nick: Replicat process is going to read the changes from the trail file and then apply them to the target system, just like any database user would. It's not doing anything special where it's going under the covers and trying to apply directly to the database blocks. It's just applying regular standard insert, update, delete, and DDL statements to that target database. A single trail file does support high volume of data replication activity depending on the type of Replicat. Replicats do preserve the boundary of their transactions. So in the situations, by default, a transaction that's on the source, let's say five inserts followed by a commit will remain five inserts followed by a commit on the target site. There are some operations and changes that do affect this, but they're not turned on by default. There are things like group transactions that allows you to group multiple transactions into a single commit. This one could actually improve performance in some cases. We also have batch SQL that can change the boundaries of a transaction as well. And then in a Parallel Replicat, you actually have the ability to split a large transaction into multiple chunks and apply those chunks in Parallel. So again, by default, it's going to preserve the boundaries, but there are ways to change that. And then the Replicats use a checkpoint table to help with recovery and to know where they're applying data and what they've done. The other thing in here is, like an Extract process can write to multiple trails and write subsets of data to each one, a Replicat can only process a single set of trail files at once. So it's going to be attached to a specific trail file like trail file AB, and will only be able to read changes from trail file AB. If I have multiple trails that need to be applied into a target system, then I have to set up multiple Replicats to handle that. 03:54 Nikita: So, what are the different Replicat types, Nick? Nick: We have three types in the product today. We have Classic Replicat, which should really only be used for testing purposes or in environments that don't support any of the other specialized Replicats. We have Coordinated Replicat, which is a high speed apply mechanism to apply data into a target system. It does have some parallelism in it, but it's user defined parallelism. And then we have our flagship and that's Parallel Replicat. And this is the most performant lowest latency Replicat that we have. 04:25 Lois: Ok. Let’s dive a little deeper into each of them, starting with the Classic Replicat. How does it work? Nick: It's pretty straightforward. You're going to have a process that reads the trail files, and then in a single threaded fashion it's going to take the trail file logical change record, convert it to an insert, update, or delete, and then apply it into that target database. Each transaction that it does is preceded by a change to the checkpoint table. So when the transaction that the Replicat is currently doing is committed, that checkpoint table update also gets committed. That way when the Replicat restarts, it knows exactly what transaction it left off and how it last applied the record. And all the Replicats work the same way with regards to checkpoint tables. They each have their own little method of ensuring that the transaction they're applying is also reflected within the checkpoint table so that when it restarts, it knows exactly where it happened. That way, if a Replicat dies in the middle of a transaction, it can be restarted without any duplicate data or without missing data. 05:29 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from multicloud, database, networking, and security to artificial intelligence and machine learning, all free for our subscribers. So, what are you waiting for? Pick a topic, head over to mylearn.oracle.com, and get started. 05:53 Nikita: Welcome back! Moving on, what about Coordinated Replicat? Nick: The Coordinated Replicat is going to read from a set of trail files. It's going to have multiple threads that do this. So you have your base thread, your coordinated thread that's going to be thread 1. It's going to process the data and apply it into that target database. You then have thread 2, 4, 5, 6, and so on. When you set up your Replicat parameter file for a Coordinated Replicat, the map commands that maps from one table on the source to a table on the target has an additional option. So you'll have an option called a range or thread range. With the range and thread range option, you can actually tell which table to go into which thread. 06:38 Lois: Can you give us an example of this? Nick: So I could say map Scott.M into thread 1 and I want Scott.Dept into thread 2. Well, this is fantastic until you realize that Scott.M and Scott.Dept have a foreign key between them or a child dependencies, parent-child relationships. What that means is that now I'm going to have to disable that foreign key on the target site, because there's no way for GoldenGate to coordinate the changes in one thread to another thread. And so you really have to be careful on how you pair your tables together. If you don't have any referential integrity on that target database, then you can use parallel coordinated Replicat to really high degrees of parallelism, and you get some very good performance out of it. Let's say that you have a table that's really got too much data for even a single thread to process, that's where the thread range comes in. And thread range command will use something like the table's primary key to split transactions on that table across multiple threads. So I can say, hey, take my table Scott.M and I want to spread transactions across threads 10, 11, 12, 13, and 14 and then spread them evenly based on the primary key. And Coordinated Replicat will do that. So you can get some very high performance numbers out of it and you can really fine tune the tables, especially if you know the amount of data coming into each one. While this does work great, we observed that a lot of customers really don't know their applications to that level of detail, and so we needed a different method to push data into that target database, where we could define the parallelism based on the database expectations. So instead of the customer having to try and figure out what are the parent-child relationships, why can't GoldenGate do it for me? And that led to Parallel Replicat.  08:26 Nikita: And what are the benefits and features of the Parallel Replicat process?  Nick: So Parallel Replicat has been around for quite a few years now. It supports most targets, it was Oracle initially, but now it's been expanded out to a lot of the non-Oracle targets and even some of the nonrelational database targets. It has absolutely the best performance of any Replicat process out there. You can use it to split large transactions as well. So if all of a sudden you have a bat

In this episode, Lois Houston and Nikita Abraham dive into key components of Oracle GoldenGate 23ai with expert insights from Nick Wagner, Senior Director of Product Management.   They break down the Distribution Service, explaining how it moves trail files between environments, replaces the classic extract pump, and ensures secure data transfer. Nick also introduces Target Initiated Paths, a method for connecting less secure environments to more secure ones, and discusses how the Receiver Service simplifies monitoring and management. The episode wraps up with a look into Initial Load, covering different methods for syncing source and target databases without downtime.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs.  Lois: Hey there! Last week, we spoke about the Extract process and today we’re going to spend time discussing the Distribution Path, Target Initiated Path, Receiver Server, and Initial Load. These are all critical components of the GoldenGate architecture, and understanding how they work together is essential for successful data replication. 00:58 Nikita: To help us navigate these topics, we’ve got Nick Wagner joining us again. Nick is a Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Thanks for being with us today. To kick things off, can you tell us what the distribution service is and how it works? Nick: A distribution path is used when we need to send trail files between two different GoldenGate environments. The distribution service replaces the extract pump that was used in GoldenGate classic architecture. And so the distribution service will send the trail files as they're being created to that receiver service and it will write the trail files over on the target system. The distribution service works in a kind of a streaming fashion, so it's constantly pulling the trail files that the extract is creating to see if there's any new data. As soon as it sees new data, it'll packet it up and send it across the network to the receiver service. It can use a couple of different methods to do this. The most secure and recommended method is using a WebSocket secure connection or WSS. If you're going between a microservices and a classic architecture, you can actually tell the distribution service to send it using the classic architecture method. In that case, it's the OGG option when you're configuring the distribution service. There's also some unsecured methods that would send the trail files in plain text. The receiver service is then responsible for taking that data and rewriting it into the trail file on the target site. 02:23 Lois: Nick, what are some of the key features and responsibilities of the distribution service? Nick: It's responsible for command deployment. So any time that you're going to actually make a command to the distribution service, it gets handled there directly. It can handle multiple commands concurrently. It's going to dispatch trail files to one or more receiver servers so you can actually have a single distribution path, send trail files to multiple targets. It can provide some lightweight filtering so you can decide which tables get sent to the target system. And it also is integrated in with our data streams, our pub and subscribe model that we've added in GoldenGate 23ai. 03:01 Lois: Interesting. And are there any protocols to remember when using the distribution service? Nick: We always recommend a secure WebSocket. You also have proxy support for use within cloud environments. And then if you're going to a classic architecture GoldenGate, you would use the Oracle GoldenGate protocol. So in order to communicate with the distribution service and send it commands, you can communicate directly from any web browser, client software-- installation is not required-- or you can also do it through the admin client if necessary, but you can do it directly through browsers. 03:33 Nikita: Ok, let's move on to the target initiated path. Nick, what is it and what does it do essentially? Nick: This is used when you're communicating from a less secure environment to a more secure environment. Often, this requires going through some sort of DMZ. In these situations, a connection cannot be established from the less secure environment into the more secure environment. It actually needs to be established from the more secure environment out. And so if we need to replicate data into a more secure environment, we need to actually have the target GoldenGate environment initiate that connection so that it can be established.  And that's what a target-initiated path does. 04:12 Lois: And how do you set it up? Nick: It's pretty straightforward to set up. You actually don't even need to worry about it on the source side. You actually set it up and configure it from the target. The receiver service is responsible for receiving the trail file data and writing it to the local trail file. In this situation, we have a target-initiated path created. And so that receiver service is going to write the trail files locally and the replicat is going to apply that data into that target system. 04:37 Nikita: I also want to ask you about the Receiver service. What is it really? Nick: Receiver service is pretty straightforward. It's a centrally controlled service. It allows you to view the status of your distribution path and replaces target side collectors that were available in the classic architecture of GoldenGate. You can also get statistics about the receiver service directly from the web UI.  You can get detailed information about these paths by going into the receiver service and identifying information like network details, transfer protocols, how many bytes it's received, how many bytes it's sent out. If you need to issue commands from the admin client to the receiver service, you can use the info command to get details about it. Info all will tell you everything that's running. And you can see that your receiver service is up and running. 05:28 Are you working towards an Oracle Certification this year? Join us at one of our certification prep live events in the Oracle University Learning Community. Get insider tips from seasoned experts and learn from others who have already taken their certifications. Go to community.oracle.com/ou to jump-start your journey towards certification today! 05:53 Nikita: Welcome back. In the last section of today’s episode, we’ll cover what Initial Load is. Nick, can you break down the basics for us? Nick: So, the initial load is really used when you need to synchronize the source and target systems. Because GoldenGate is designed for 24/7 environments, we need to be able to do that initial load without taking downtime on the source. And so all the methods that we talk about do not require any downtime for that source database. 06:18 Lois: How do you do the initial load? Nick: So there's a couple of different ways to do the initial load. And it really depends on what your topology is. If I'm doing like-to-like replication in a homogeneous environment, we'll say Oracle-to-Oracle, the best options are to use something that's integrated with GoldenGate, some sort of precise instantiation method that does not require HandleCollisions. That's something like a database backup and restoring it to a specific SDN or CSN value using a Database Snapshot. Or in some cases, we can use Oracle Data Pump integration with GoldenGate. There are some less precise instantiation options, which do require HandleCollisions. We also have dissimilar initial load methods. And this is typically when you're going between heterogeneous environments. When my source and target databases don't match and there isn't any kind of fast unload or fast load utility that I could use between those two databases. In almost all cases, this does require HandleCollisions to be used. 07:16 Nikita: Got it. So, with so many options available, are there any advantages to using GoldenGate's own initial load method?  Nick: While some databases do have very good fast load and unload utilities, there are some advantages to using GoldenGate's own initial load method. One, it supports heterogeneous replication environments. So if I'm going from Postgres to Oracle, it'll do all the data type transformation, character set transformation for me. It doesn't require any downtime, if certain conditions are met.  It actually performs transformation as the data is loaded, too, as well as filtering. And so any transformation that you would be doing in your normal transaction log replication or CDC replication can also go through the same transformation for the initial load process. GoldenGate's initial load process does read directly from the source tables. And it fetches the data in arrays. It also uses parallel processing to speed up the replication. It does also handle activity on the source tables during the initial load process, so you do not need to worry about quiescing that source database. And a lot of the initial load methods directly built into GoldenGate support distributed application analytics targets, includi

The Extract process is the heart of Oracle GoldenGate 23ai, capturing data changes with precision. In this episode, Lois Houston and Nikita Abraham sit down with Nick Wagner, Senior Director of Product Management, to break down Extract’s role, architecture, and best practices.   Learn how Extract works across different setups, from running on source databases to using a Hub model for greater flexibility. Additionally, understand how trail files, parameter files, and naming conventions impact performance.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we spoke about installing GoldenGate and today, we’re diving into the Extract process. We’ve discussed it briefly in an earlier episode, but to recap, the Extract process captures changes from the source database and writes them to a trail file. 00:54 Lois: Joining us again is Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Before we get into the Extract process, can you walk us through the different architecture options available for GoldenGate. Let’s start with when GoldenGate is installed on the same server as the source database. What are the benefits of this architecture? Nick: There's a couple of advantages to this. It means that GoldenGate can use the same resources on that source database. It means that you don't need another host to support the GoldenGate environment. It also means that GoldenGate can use a bequeathed connection to connect from the Extract process into the source database to make it run faster. The restrictions on this are that the Replicat process is highly communicative with the target database. What that really means is that the Replicat process is constantly doing lots of little transactions. And so the network latency between the Replicat process and the target database should really be around 4 milliseconds or less for optimal performance. So that means that a lot of people can't really run GoldenGate on the source system, even though it's an option, because they need that Replicat latency performance. And so they'll often install GoldenGate on the same server as the target database. In this case, they can use the Replicat to connect using a bequeath connection to that target system, you know that it's going to be highly performant and that latency is not going to be an issue. This works really well because the Extract process has actually been optimized to do remote capture. And so it's actually able to handle 80 milliseconds round trip ping time or less between the actual Extract process and the source database itself. And so a lot of customers will opt for this method, where they're actually running GoldenGate away from the target, or excuse me, away from the source database. 02:44 Nikita: Interesting. And is there an option where you don’t need to install GoldenGate on the actual source or target database? Nick: We also have another architecture pattern called a Hub model. And this is what you would see in something like OCI GoldenGate or OCI Marketplace, or even in third party clouds environments where you don't have the ability to install GoldenGate on the actual source or target database. In these cases, GoldenGate is just going to run on a virtual machine or an environment that you have set up specifically for GoldenGate. Now, this GoldenGate Hub doesn't need to have any database software installed. It doesn't need to have any database information on it. It's simply working as a client. So GoldenGate Extract process is a client connecting into the source database and the Replicat is a client connecting into the target database. And this really gives you a lot of flexibility. However, in some cases, there may be too much of a distance, so you won't be able to get both less than 80 milliseconds on the source side in less than 4 milliseconds on the round trip on the target side. And so in that case, you can have multiple GoldenGate Hubs. And so you would have a Hub on the Extract side and another Hub on the Replicat side. And all these are fully accessible. In this case, you'll actually use the distribution service to send the trail files from one system to another.  04:00 Lois: So, coming to the Extract process, what does it actually do?  Nick: The Extract process is configured to capture changes from that source database. In different terminology, it can subscribe to a topic if we're pulling data out of a Kafka queue or a topic or some messaging system like a JMS queue and relational database language, we're pulling database from the database transaction logs. There's a lot of different sources and targets. You can always use the GoldenGate Certification Matrix to determine which sources and targets are supported, and where we can extract data from. The capture process also connects to the source table for initial loads. When we do the initial load, instead of reading from the transaction logs, GoldenGate is actually going to do a select star on that table to get the information it needs for that load. 04:49 Lois: And what about the Extract process group?  Nick: The process group is kind of a grouping of the process itself, which is either going to be my Extract or Replicat and associated files. So in an Extract environment, we have our parameter file and a report file and our checkpoint files. The parameter file, the .prm file, is going to list out which objects we're going to capture and how we're going to capture that data. It also controls what we're going to be writing to the trail file and where that trail file exists. The report file is really just a log of what's going on in that Extract process, how it's working, what tables it's encountered. It's used for any troubleshooting to make sure everything is running smoothly. And then you also have the checkpoint files. The checkpoint files and report files should not be modified by the user, the parameter file can be. The checkpoint files are going to include information about where that process is reading from, where it's writing to, and any open transaction that it's tracking as part of the bounded recovery or cache manager functionality. 05:54 Nikita: How do you go about creating an Extract group? Nick: The Extract group can be created by doing an Add Extract command or through the UI. Each Extract must also have a unique name. On the Extract process side, there is an eight-character hard limit for the name itself. And so, you can’t have an Extract process called my Extract for today is called Nick. More than eight characters. 06:17 Lois: Nick, I was wondering, is there a simple way to identify what an Extract or Replicat is doing? Nick: If you need something to help identify what that Extract or Replicat is doing or the description of it, we do have a description field. So when you do the Add Extract or Add Replicat, there is a DESC field that allows you to add more details in. And this is really key because it allows you to put a lot more information that’s going to show up in all the log files at the service manager level. And any time you do an info on the service it’ll also bring up that description field so you can see what’s going on. That way, if you get an alert, a watch, you need to keep track of something you can easily identify what that process is doing and what it’s replicating for.  07:06 Adopting a multicloud strategy is a big step towards future-proofing your business and we’re here to help you navigate this complex landscape. With our suite of courses, you'll gain insights into network connectivity, security protocols, and the considerations of working across different cloud platforms. Start your journey to multicloud today by visiting mylearn.oracle.com. 07:32 Nikita: Welcome back! Before the break, we were talking about the description field, which helps identify what the Extract is doing. Nick, are there any best practices to keep in mind when naming a group? Nick: You also don't want to use any special characters when naming the group, especially you know things like slashes or dashes. You don't want to use spaces in them, just really stick to alphanumeric characters only. The group names are also case insensitive, so EDEPT, all capitalized is the same as edept lowercase. The other thing that you don't want to do and this isn't a hard restriction, it's just more of a friendly reminder is don't end your group with a numeric value. The report files themselves end in numeric values, so you'll have a report file, 0123456789, and so on. If you were to end your group name with a numeric value, then it can often be confused for a report file. And so you don't want to really do that. But otherwise you're free to call it whatever you want. 08:39 Lois: Got it. What about naming conventions? Are there any rules that apply? Nick: You can use whatever naming convention you want, but again, try and follow these best practices. No strange characters and don't end your process names with a numeric value. 08:53 Nikita: Can you explain the role of parameter and trail files in the Extract process? Nick: The

Installing Oracle GoldenGate 23ai is more than just running a setup file—it’s about preparing your system for efficient, reliable data replication. In this episode, Lois Houston and Nikita welcome back Nick Wagner to break down system requirements, storage considerations, and best practices for installing GoldenGate.   You’ll learn how to optimize disk space, manage trail files, and configure network settings to ensure a smooth installation.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   -------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Hello and welcome to Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and I’m joined by Lois Houston, Director of Innovation Programs.  Lois: Hi there! Last week, we took a close look at the security strategies of Oracle GoldenGate 23ai. In this episode, we’ll discuss all aspects of installing GoldenGate. 00:48 Nikita: That’s right, Lois. And back with us today is Nick Wagner, Senior Director of Product Management for GoldenGate at Oracle. Hi Nick! I’m going to get straight into it. What are the system requirements for a typical GoldenGate installation? Nick: As far as system requirements, we're going to split that into two sections. We've got an operating system requirements and a storage requirements. So with memory and disk, and I know that this isn't the answer you want, but the answer is that it varies. With GoldenGate, the amount of CPU usage that is required depends on the number of extracts and replicats. It also depends on the number of threads that you're going to be using for those replicats. Same thing with RAM and disk usage. That's going to vary on the transaction sizes and the number of long running transactions. 01:35 Lois: And how does the recovery process in GoldenGate impact system resources?  Nick: You've got two things that help the extract recovery. You've got the bonded recovery that will store transactions over a certain length of time to disk. It also has a cache manager setting that determines what gets written to disk as part of open transactions. It's not just the simple answer as, oh, it needs this much space. GoldenGate also needs to store trail files for the data that it's moving across. So if there's network latency, or if you expect a certain network outage, or you have certain SLAs for the target database that may not be met, you need to make sure that GoldenGate has enough room to store its trail files as it's writing them. The good news about all this is that you can track it. You can use parameters to set them. And we do have some metrics that we'll provide to you on how to size these environments. So a couple of things on the disk usage. The actual installation of GoldenGate is about 1 to 1.5 gig in size, depending on which version of GoldenGate you're going to be using and what database. The trail files themselves, they default to 500 megabytes apiece. A lot of customers keep them on disk longer than they're necessary, and so there's all sorts of purging options available in GoldenGate. But you can set up purge rules to say, hey, I want to get rid of my trail files as soon as they're not needed anymore. But you can also say, you know what? I want to keep my trail files around for x number of days, even if they're not needed. That way they can be rebuilt. I can restore from any previous point in time. 03:15 Nikita: Let’s talk a bit more about trail files. How do these files grow and what settings can users adjust to manage their storage efficiently? Nick: The trail files grow at about 30% to 35% of the generated redo log data. So if I'm generating 100 gigabytes of redo an hour, then you can expect the trail files to be anywhere from 30 to 35 gigabytes an hour of generated data. And this is if you're replicating everything. Again, GoldenGate's got so many different options. There's so many different ways to use it. In some cases, if you're going to a distributed applications and analytics environment, like a Databricks or a Snowflake, you might want to write more information to the trail file than what's necessary. Maybe I want additional information, such as when this change happened, who the user was that made that change. I can add specific token data. You can also tell GoldenGate to log additional records or additional columns to the trail file that may not have been changed. So I can always say, hey, GoldenGate, replicate and store the entire before and after image of every single row change to your trail file, even if those columns didn't change. And so there's a lot of different ways to do it there. But generally speaking, the default settings, you're looking at about 30% to 35% of the generated redo log value. System swap can fill up quickly. You do want this as a dedicated disk as well. System swap is often used for just handling of the changes, as GoldenGate flushes data from memory down to disk. These are controlled by a couple of parameters. So because GoldenGate is only writing committed activity to the trail file, the log reader inside the database is actually giving GoldenGate not only committed activity but uncommitted activity, too. And this is so it can stay very high speed and very low latency. 05:17 Lois: So, what are the parameters? Nick: There's a cache manager overall feature, and there's a cache directory. That directory controls where that data is actually stored, so you can specify the location of the uncommitted transactions. You can also specify the cache size. And there's not only memory settings here, but there's also disk settings. So you can say, hey, once a cache size exceeds a certain memory usage, then start flushing to disk, which is going to be slower. This is for systems that maybe have less memory but more high-speed disk. You can optimize these parameters as necessary. 05:53 Nikita: And how does GoldenGate adjust these parameters? Nick: For most environments, you're just going to leave them alone. They're automatically configured to look at the system memory available on that system and not use it all. And then as soon as necessary, it'll overflow to disk. There's also intelligent settings built within these parameters and within the cache manager itself that if it starts seeing a lull in activity or your traditional OLTP type responses to actually free up the memory that it has allocated. Or if it starts seeing more activity around data warehousing type things where you're doing large transactions, it'll actually hold on to memory a little bit longer. So it kinda learns as it goes through your environment and starts replicating data. 06:37 Lois: Is there anything else you think we should talk about before we move on to installing GoldenGate?  Nick: There's a couple additional things you need to think of with the network as well. So when you're deploying GoldenGate, you definitely want it to use the fastest network.  GoldenGate can also use a reverse proxy, especially important with microservices. Reverse proxy, typically we recommend Nginx. And it allows you to access any of the GoldenGate microservices using a single port.  GoldenGate also needs either host names or IP addresses to do its communication and to ensure the system is available. It does a lot of communication through TCP and IP as well as WSS. And then it also handles firewalls. So you want to make sure that the firewalls are open for ingress and egress for GoldenGate, too. There's a couple of different privileges that GoldenGate needs when you go to install it. You'll want to make sure that GoldenGate has the ability to write to the home where you're installing it. That's kind of obvious, but we need to say it anyways. There's a utility called oggca.sh. That's the GoldenGate Configuration Assistant that allows you to set up your first deployments and manage additional deployments. That needs permissions to write to the directories where you're going to be creating the new deployments. The extract process needs connection and permissions to read the transaction logs or backups. This is not important for Oracle, but for non-Oracle it is. And then we also recommend a dedicated database user for the extract and replicat connections. 08:15 Are you keen to stay ahead in today's fast-paced world? We’ve got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you’re always in the know, we offer New Features courses that give you an insider’s look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started. 08:41 Nikita: Welcome back! So Nick, how do we get started with the installation?  Nick: So when we go to the install, the first thing you're going to do is go ahead and go to Oracle's website and download the software. Because of the way that GoldenGate works, there's only a couple moving parts. You saw the microservices. There's five or six of them. You have your extract, your replicat, your distribution service, trail files. There's not a lot of moving components. So if something does go wrong, usually it affects multiple customers. And so it's very important that when you go to install GoldenGate, you're using the most recent bundle patch. And you can find this within My Oracle Support. It's not always available directly from OTN

GoldenGate 23ai takes security seriously, and this episode unpacks everything you need to know. GoldenGate expert Nick Wagner breaks down how authentication, access roles, and encryption protect your data.   Learn how GoldenGate integrates with identity providers, secures communication, and keeps passwords out of storage. Understand how trail files work, why they only store committed data, and how recovery processes prevent data loss.   Whether you manage replication or just want to tighten security, this episode gives you the details to lock things down without slowing operations.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Welcome, everyone! This is our fourth episode on Oracle GoldenGate 23ai. Last week, we discussed the terminology, different processes and what they do, and the architecture of the product at a high level. Today, we have Nick Wagner back with us to talk about the security strategies of GoldenGate. 00:56 Lois: As you know by now, Nick is a Senior Director of Product Management for GoldenGate at Oracle. He’s played a key role as one of the product designers behind the latest version of GoldenGate. Hi Nick! Thank you for joining us again. Can you tell us how GoldenGate takes care of data security? Nick: So GoldenGate authentication and authorization is done in a couple of different ways. First, we have user credentials for GoldenGate for not only the source and target databases, but also for GoldenGate itself. We have integration with third-party identity management products, and everything that GoldenGate does can be secured. 01:32 Nikita: And we must have some access roles, right? Nick: There's four roles built into the GoldenGate product. You have your security role, administrator, operator, and user. They're all hierarchical. The most important one is the security user. This user is going to be the one that provides the administrative tasks. This user is able to actually create additional users and assign roles within the product. So do not lose this password and this user is extremely important. You probably don't want to use this security user as your everyday user. That would be your administrator. The administrator role is able to perform all administrative tasks within GoldenGate. So not only can they go in and create new extracts, create new replicats, create new distribution services, but they can also start and stop them. And that's where the operator role is and the user role. So the operator role allows you to go in and start/stop processes, but you can't create any new ones, which is kind of important. So this user would be the one that could go in and suspend activity. They could restart activity. But they can't actually add objects to replication. The user role is really a read-only role. They can come in. They can see what's going on. They can look at the log files. They can look at the alerts. They can look at all the watches and see exactly what GoldenGate is doing. But they're unable to make any changes to the product itself. 02:54 Lois: You mentioned the roles are hierarchical in nature. What does that mean? Nick: So anything that the user role does can be done by the operator. Anything that the operator and user roles can do can be done by the administrator. And anything that the user, operator, and administrator roles do can be done by the security role. 03:11 Lois: Ok. So, is there a single sign-on available for GoldenGate? Nick: We also have a password plugin for GoldenGate Connections. A lot of customers have asked for integration with whatever their single sign-on utility is, and so GoldenGate now has that with GoldenGate 23ai. So these are customer-created entities. So, we have some examples that you can use in our documentation on how to set up an identity provider or a third-party identity provider with GoldenGate. And this allows you to ensure that your corporate standards are met. As we started looking into this, as we started designing it, every single customer wanted something different. And so instead of trying to meet the needs for every customer and every possible combination of security credentials, we want you to be able to design it the way you need it. The passwords are never stored. They're only retrieved from the identity provider by the plugin itself. 04:05 Nikita: That’s a pretty important security aspect…that when it’s time to authenticate a user, we go to the identity provider. Nick: We're going to connect in and see if that password is matching. And only then do we use it. And as soon as we detect that it's matched, that password is removed. And then for the extract and replicats themselves, you can also use it for the database, data source, and data target connections, as well as for the GoldenGate users. So, it is a full-featured plugin. So, our identity provider plugin works with IAM as well as OAM. These are your standard identity manager authentication methods. The standard one is OAuth 2, as well as OIDC. And any Identity Manager that uses that is able to integrate with GoldenGate. 04:52 Lois: And how does this work? Nick: The way that it works is pretty straightforward. Once the user logs into the database, we're going to hand off authentication to the identity provider. Once the identity provider has validated that user's identity and their credentials, then it comes back to GoldenGate and says that user is able to log in to either GoldenGate or the application or the database. Once the user is logged in, we get that confirmation that's been sent out and they can continue working through GoldenGate. So, it's very straightforward on how it works. There's also a nice little UI that will help set up each additional user within those systems. All the communication is also secured as well. So any communication done through any of the GoldenGate services is encrypted using HTTPS. All the REST calls themselves are all done using HTTPS as well. All the data protection calls and all the communication across the network when we send data across a distribution service is encrypted using a secure WebSocket. And there's also trail file encryption at the operating system level for data at REST. So, this really gives you the full level of encryption for customers that need that high-end security. GoldenGate does have an option for FIPS 140-2 compliance as well. So that's even a further step for most of those customers. 06:12 Nikita: That’s impressive! Because we want to maintain the highest security standards, right? Especially when dealing with sensitive information. I now want to move on to trail files. In our last episode, we briefly spoke about how they serve as logs that record and track changes made to data. But what more can you tell us about them, Nick? Nick: There's two different processes that write to the trail files. The extract process will write to the trail file and the receiver service will write to the trail file. The extract process is going to write to the trail file as it's pulling data out of that source database. Now, the extract process is controlled by a parameter file, that says, hey, here's the exact changes that I'm going to be pulling out. Here's the tables. Here's the rows that I want. As it's pulling that data out and writing it to the trail files, it's ensuring that those trail files have enough information so that the replicat process can actually construct a SQL statement and apply that change to that target platform. And so there's a lot of ways to change what's actually stored in those trail files and how it's handled. The trail files can also be used for initial loads. So when we do the initial load through GoldenGate, we can grab and write out the data for those tables, and that excludes the change data. So initial loads is pulling the data directly from the tables themselves, whereas ongoing replication is pulling it from the transaction logs. 07:38 Lois: But do we need to worry about rollbacks? Nick: Our trail files contain committed data only and all data is sequential. So this is two important things. Because it contains committed data only, we don't need to worry about rollbacks. We also don't need to worry about position within that trail file because we know all data is sequential. And so as we're reading through the trail file, we know that anything that's written in a prior location in that trial file was committed prior to something else. And as we get into the recovery aspects of GoldenGate, this will all make a lot more sense. 08:13 Lois: Before we do that, can you tell us about the naming of trail files? Nick: The trail files as far as naming, because these do reside on the operating system, you start with a two-letter trail file abbreviation and then a nine-digit sequential value. So, you almost look at it as like an archive log from Oracle, where we have a prefix and then an affix, which is numeric. Same kind of thing. So, we have our two-letter, in this case, an ab, and then we have a nine-digit number. 08:47 Transform the way you work with Oracle Database 23ai! This cutting-edge technology brings