AWS re:Invent 2017

AWS has launched Amazon Sumerian. Sumerian lets you create and run virtual reality (VR), augmented reality (AR), and 3D applications quickly and easily without requiring any specialized programming or 3D graphics expertise. In this session, we will introduce you to Sumerian, and how you can build highly immersive and interactive scenes for the enterprise that run on popular hardware such as Oculus Rift, HTC Vive, and iOS mobile devices.

AWS has launched Amazon Sumerian. Sumerian lets you create and run virtual reality (VR), augmented reality (AR), and 3D applications quickly and easily without requiring any specialized programming or 3D graphics expertise. In this session, we will dive deep into details about Sumerian so you can see what's under the hood. We will cover creating a project, using the visual state machine, connecting an Amazon Sumerian scene to AWS services, and using a Sumerian Host to add presence to your applications.

Join us to hear about our strategy for driving machine learning innovation for our customers and learn what's new from AWS in the machine learning space. Swami Sivasubramanian, VP of Amazon Machine Learning, will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.

Andy Jassy, CEO of Amazon Web Services, delivers his AWS re:Invent 2017 keynote, featuring the latest news and announcements, including the launches of Amazon Elastic Containers for Kubernetes (EKS), AWS Fargate, Aurora Multi-Master, Aurora Serverless, DynamoDB Global Tables, Amazon Neptune, S3 Select, Amazon Sagemaker, AWS DeepLens, Amazon Rekognition Video, Amazon Kinesis Video Streams, Amazon Transcribe, Amazon Translate, Amazon Comprehend, AWS IoT 1-Click, AWS IoT Device Management, AWS IoT Device Defender, AWS IoT Analytics, Amazon FreeRTOS, and Greengrass ML Inference. Guest speakers include Dr. Matt Wood, of AWS; Roy Joseph, of Goldman Sachs; Mark Okerstrom, of Expedia; and Michelle McKenna-Doyle, of the NFL.

Watch Werner Vogels deliver his AWS re:Invent 2017 keynote, featuring the launch of Alexa for Business, AWS Cloud9, new AWS Lambda features, and Serverless App Repository.

Watch Peter DeSantis, VP, AWS Global Infrastructure, in the Tuesday Night Live keynote, featuring Brian Mathews, of Autodesk, and Greg Peters, of Netflix.

In this session, we simplify big data processing as a data bus comprising various stages: collect, store, process, analyze, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.

Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. In this session, we show you how to incorporate serverless concepts into your big data architectures. We explore the concepts behind and benefits of serverless architectures for big data, looking at design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness and share a reference architecture using a combination of cloud and open source technologies to solve your big data problems. Topics include: use cases and best practices for serverless big data applications; leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, Amazon Kinesis, AWS Lambda, Amazon Athena, and Amazon EMR; and serverless ETL, event processing, ad hoc analysis, and real-time analytics.

To win in the marketplace and provide differentiated customer experiences, businesses need to be able to use live data in real time to facilitate fast decision making. In this session, you learn common streaming data processing use cases and architectures. First, we give an overview of streaming data and AWS streaming data capabilities. Next, we look at a few customer examples and their real-time streaming applications. Finally, we walk through common architectures and design patterns of top streaming data use cases.

Data speaks. Discover how Ivy Tech, the nation's largest singly accredited community college, uses AWS to gather, analyze, and take action on student behavioral data for the betterment of over 3,100 students. This session outlines the process from inception to implementation across the state of Indiana and highlights how Ivy Tech's model can be applied to your own complex business problems.

Just as a picture is worth a thousand words, a visual is worth a thousand data points.  A key aspect of our ability to gain insights from our data is to look for patterns, and these patterns are often not evident when we simply look at data in tables. The right visualization will help you gain a deeper understanding in a much quicker timeframe.  In this session, we will show you how to quickly and easily visualize your data using Amazon QuickSight.  We will show you how you can connect to data sources, generate custom metrics and calculations, create comprehensive business dashboards with various chart types, and setup filters and drill downs to slice and dice the data.

In this session, learn how Cox Automotive is using Splunk Cloud for real time visibility into its AWS and hybrid environments to achieve near instantaneous MTTI, reduce auction incidents by 90%, and proactively predict outages. We also introduce a highly anticipated capability that allows you to ingest, transform, and analyze data in real time using Splunk and Amazon Kinesis Firehose to gain valuable insights from your cloud resources. It's now quicker and easier than ever to gain access to analytics-driven infrastructure monitoring using Splunk Enterprise & Splunk Cloud. Session sponsored by Splunk

Historically, silos of data, analytics, and processes across functions, stages of development, and geography created a barrier to R&D efficiency. Gathering the right data necessary for decision-making was challenging due to issues of accessibility, trust, and timeliness. In this session, learn how Takeda is undergoing a transformation in R&D to increase the speed-to-market of high-impact therapies to improve patient lives. The Data and Analytics Hub was built, with Deloitte, to address these issues and support the efficient generation of data insights for functions such as clinical operations, clinical development, medical affairs, portfolio management, and R&D finance. In the AWS hosted data lake, this data is processed, integrated, and made available to business end users through data visualization interfaces, and to data scientists through direct connectivity. Learn how Takeda has achieved significant time reductions—from weeks to minutes—to gather and provision data that has the potential to reduce cycle times in drug development. The hub also enables more efficient operations and alignment to achieve product goals through cross functional team accountability and collaboration due to the ability to access the same cross domain data. Session sponsored by Deloitte

As the nation's only high-speed intercity passenger rail provider, Amtrak needs to know critical information to run their business such as: Who's onboard any train at any time? How are booking and revenue trending? Amtrak was faced with unpredictable and often slow response times from existing databases, ranging from seconds to hours; existing booking and revenue dashboards were spreadsheet-based and manual; multiple copies of data were stored in different repositories, lacking integration and consistency; and operations and maintenance (O&M) costs were relatively high. Join us as we demonstrate how Deloitte and Amtrak successfully went live with a cloud-native operational database and analytical datamart for near-real-time reporting in under six months. We highlight the specific challenges and the modernization of architecture on an AWS native Platform as a Service (PaaS) solution. The solution includes cloud-native components such as AWS Lambda for microservices, Amazon Kinesis and AWS Data Pipeline for moving data, Amazon S3 for storage, Amazon DynamoDB for a managed NoSQL database service, and Amazon Redshift for near-real time reports and dashboards. Deloitte's solution enabled “at scale” processing of 1 million transactions/day and up to 2K transactions/minute. It provided flexibility and scalability, largely eliminate the need for system management, and dramatically reduce operating costs. Moreover, it laid the groundwork for decommissioning legacy systems, anticipated to save at least $1M over 3 years. Session sponsored by Deloitte

In this session, we detail Sysco's journey from a company focused on hindsight-based reporting to one focused on insights and foresight. For this shift, Sysco moved from multiple data warehouses to an AWS ecosystem, including Amazon Redshift, Amazon EMR, AWS Data Pipeline, and more. As the team at Sysco worked with Tableau, they gained agile insight across their business. Learn how Sysco decided to use AWS, how they scaled, and how they became more strategic with the AWS ecosystem and Tableau. Session sponsored by Tableau

Learn how customers are leveraging AWS to better position their enterprises for the digital transformation journey. In this session, you hear about: operations and process; the SAP transformation journey including architecting, migrating, running SAP on AWS; complete automation and management of the AWS layer using AWS native services; and a customer example. We also discuss the challenges of migration to the cloud and a managed services environment; the benefits to the customer of the new operating model; and lessons learned. By the end of the session, you understand why you should consider AWS for your next SAP platform, how to get there when you are ready and some best practices to manage your SAP systems on AWS. session sponsored by DXC Technology

With customers demanding relevant and real-time experiences across a range of devices, digital businesses are looking to gather user data at scale, understand this data, and respond to customer needs instantly. This requires tools that can record large volumes of user data in a structured fashion, and then instantly make this data available to generate insights. In this session, we demonstrate how you can use Amazon Pinpoint to capture user data in a structured yet flexible manner. Further, we demonstrate how this data can be set up for instant consumption using services like Amazon Kinesis Firehose and Amazon Redshift. We walk through example data based on real world scenarios, to illustrate how Amazon Pinpoint lets you easily organize millions of events, record them in real-time, and store them for further analysis.

Amazon Kinesis Video Streams makes it easy to securely stream video from connected devices to AWS for analytics, machine learning (ML), and other processing. In this session, we introduce Kinesis Video Streams and its key features, and review common use cases including smart home, smart city, industrial automation, and computer vision. We also discuss how you can use the Kinesis Video Streams parser library to work with the output of video streams to power popular deep learning frameworks. Lastly, Abeja, a leading Japanese artificial intelligence (AI) solutions provider, talks about how they built a deep-learning system for the retail industry using Kinesis Video Streams to deliver better shopping experience.

Reducing the time to get actionable insights from data is important to all businesses, and customers who employ batch data analytics tools are exploring the benefits of streaming analytics. Learn best practices to extend your architecture from data warehouses and databases to real-time solutions. Learn how to use Amazon Kinesis to get real-time data insights and integrate them with Amazon Aurora, Amazon RDS, Amazon Redshift, and Amazon S3. The Amazon Flex team describes how they used streaming analytics in their Amazon Flex mobile app, used by Amazon delivery drivers to deliver millions of packages each month on time. They discuss the architecture that enabled the move from a batch processing system to a real-time system, overcoming the challenges of migrating existing batch data to streaming data, and how to benefit from real-time analytics.

IoT and big data have made their way out of industrial applications, general automation, and consumer goods, and are now a valuable tool for improving consumer engagement across a number of industries, including media, entertainment, and sports. The low cost and ease of implementation of AWS analytics services and AWS IoT have allowed AGT, a leader in IoT, to develop their IoTA analytics platform. Using IoTA, AGT brought a tailored solution to EuroLeague Basketball for real-time content production and fan engagement during the 2017-18 season. In this session, we take a deep dive into how this solution is architected for secure, scalable, and highly performant data collection from athletes, coaches, and fans. We also talk about how the data is transformed into insights and integrated into a content generation pipeline. Lastly, we demonstrate how this solution can be easily adapted for other industries and applications.