DiscoverData Engineering Podcast
Data Engineering Podcast

Data Engineering Podcast

Author: Tobias Macey

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Description

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.
111 Episodes
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Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditional data warehouse engines, the features that allow you to scale your usage dynamically, and how it allows for a shift in your workflow from ETL to ELT. If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen.
The financial industry has long been driven by data, requiring a mature and robust capacity for discovering and integrating valuable sources of information. Citadel is no exception, and in this episode Michael Watson and Robert Krzyzanowski share their experiences managing and leading the data engineering teams that power the business. They shared helpful insights into some of the challenges associated with working in a regulated industry, organizing teams to deliver value rapidly and reliably, and how they approach career development for data engineers. This was a great conversation for an inside look at how to buld and maintain a data driven culture.
The team at Sentry has built a platform for anyone in the world to send software errors and events. As they scaled the volume of customers and data they began running into the limitations of their initial architecture. To address the needs of their business and continue to improve their capabilities they settled on Clickhouse as the new storage and query layer to power their business. In this episode James Cunningham and Ted Kaemming describe the process of rearchitecting a production system, what they learned in the process, and some useful tips for anyone else evaluating Clickhouse.
With the constant evolution of technology for data management it can seem impossible to make an informed decision about whether to build a data warehouse, or a data lake, or just leave your data wherever it currently rests. What's worse is that any time you have to migrate to a new architecture, all of your analytical code has to change too. Thankfully it's possible to add an abstraction layer to eliminate the churn in your client code, allowing you to evolve your data platform without disrupting your downstream data users. In this episode AtScale co-founder and CTO Matthew Baird describes how the data virtualization and data engineering automation capabilities that are built into the platform free up your engineers to focus on your business needs without having to waste cycles on premature optimization. This was a great conversation about the power of abstractions and appreciating the value of increasing the efficiency of your data team.
The practice of data management is one that requires technical acumen, but there are also many policy and regulatory issues that inform and influence the design of our systems. With the introduction of legal frameworks such as the EU GDPR and California's CCPA it is necessary to consider how to implement data protectino and data privacy principles in the technical and policy controls that govern our data platforms. In this episode Karen Heaton and Mark Sherwood-Edwards share their experience and expertise in helping organizations achieve compliance. Even if you aren't subject to specific rules regarding data protection it is definitely worth listening to get an overview of what you should be thinking about while building and running data pipelines.
As data engineers the health of our pipelines is our highest priority. Unfortunately, there are countless ways that our dataflows can break or degrade that have nothing to do with the business logic or data transformations that we write and maintain. Sean Knapp founded Ascend to address the operational challenges of running a production grade and scalable Spark infrastructure, allowing data engineers to focus on the problems that power their business. In this episode he explains the technical implementation of the Ascend platform, the challenges that he has faced in the process, and how you can use it to simplify your dataflow automation. This is a great conversation to get an understanding of all of the incidental engineering that is necessary to make your data reliable.
Despite the fact that businesses have relied on useful and accurate data to succeed for decades now, the state of the art for obtaining and maintaining that information still leaves much to be desired. In an effort to create a better abstraction for building data applications Nick Schrock created Dagster. In this episode he explains his motivation for creating a product for data management, how the programming model simplifies the work of building testable and maintainable pipelines, and his vision for the future of data programming. If you are building dataflows then Dagster is definitely worth exploring.
The scale and complexity of the systems that we build to satisfy business requirements is increasing as the available tools become more sophisticated. In order to bridge the gap between legacy infrastructure and evolving use cases it is necessary to create a unifying set of components. In this episode Dipti Borkar explains how the emerging category of data orchestration tools fills this need, some of the existing projects that fit in this space, and some of the ways that they can work together to simplify projects such as cloud migration and hybrid cloud environments. It is always useful to get a broad view of new trends in the industry and this was a helpful perspective on the need to provide mechanisms to decouple physical storage from computing capacity.
Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. Dataform is a platform that helps you apply engineering principles to your data transformations and table definitions, including unit testing SQL scripts, defining repeatable pipelines, and adding metadata to your warehouse to improve your team's communication. In this episode CTO and co-founder of Dataform Lewis Hemens joins the show to explain his motivation for creating the platform and company, how it works under the covers, and how you can start using it today to get your data warehouse under control.
The process of exposing your data through a SQL interface has many possible pathways, each with their own complications and tradeoffs. One of the recent options is Rockset, a serverless platform for fast SQL analytics on semi-structured and structured data. In this episode CEO Venkat Venkataramani and SVP of Product Shruti Bhat explain the origins of Rockset, how it is architected to allow for fast and flexible SQL analytics on your data, and how their serverless platform can save you the time and effort of implementing portions of your own infrastructure.
Building an end-to-end pipeline for your machine learning projects is a complex task, made more difficult by the variety of ways that you can structure it. Kedro is a framework that provides an opinionated workflow that lets you focus on the parts that matter, so that you don't waste time on gluing the steps together. In this episode Tom Goldenberg explains how it works, how it is being used at Quantum Black for customer projects, and how it can help you structure your own. Definitely worth a listen to gain more understanding of the benefits that a standardized process can provide.
Object storage is quickly becoming the unifying layer for data intensive applications and analytics. Modern, cloud oriented data warehouses and data lakes both rely on the durability and ease of use that it provides. S3 from Amazon has quickly become the de-facto API for interacting with this service, so the team at MinIO have built a production grade, easy to manage storage engine that replicates that interface. In this episode Anand Babu Periasamy shares the origin story for the MinIO platform, the myriad use cases that it supports, and the challenges that they have faced in replicating the functionality of S3. He also explains the technical implementation, innovative design, and broad vision for the project.
The conventional approach to analytics involves collecting large amounts of data that can be cleaned, followed by a separate step for analysis and interpretation. Unfortunately this strategy is not viable for handling real-time, real-world use cases such as traffic management or supply chain logistics. In this episode Simon Crosby, CTO of Swim Inc., explains how the SwimOS kernel and the enterprise data fabric built on top of it enable brand new use cases for instant insights. This was an eye opening conversation about how stateful computation of data streams from edge devices can reduce cost and complexity as compared to batch oriented workflows.
The first stage in every data project is collecting information and routing it to a storage system for later analysis. For operational data this typically means collecting log messages and system metrics. Often a different tool is used for each class of data, increasing the overall complexity and number of moving parts. The engineers at Timber.io decided to build a new tool in the form of Vector that allows for processing both of these data types in a single framework that is reliable and performant. In this episode Ben Johnson and Luke Steensen explain how the project got started, how it compares to other tools in this space, and how you can get involved in making it even better.
Data professionals are working in a domain that is rapidly evolving. In order to stay current we need access to deeply technical presentations that aren't burdened by extraneous marketing. To fulfill that need Pete Soderling and his team have been running the Data Council series of conferences and meetups around the world. In this episode Pete discusses his motivation for starting these events, how they serve to bring the data community together, and the observations that he has made about the direction that we are moving. He also shares his experiences as an investor in developer oriented startups and his views on the importance of empowering engineers to launch their own companies.
Data engineers are responsible for building tools and platforms to power the workflows of other members of the business. Each group of users has their own set of requirements for the way that they access and interact with those platforms depending on the insights they are trying to gather. Benn Stancil is the chief analyst at Mode Analytics and in this episode he explains the set of considerations and requirements that data analysts need in their tools and. He also explains useful patterns for collaboration between data engineers and data analysts, and what they can learn from each other.
Managing big data projects at scale is a perennial problem, with a wide variety of solutions that have evolved over the past 20 years. One of the early entrants that predates Hadoop and has since been open sourced is the HPCC (High Performance Computing Cluster) system. Designed as a fully integrated platform to meet the needs of enterprise grade analytics it provides a solution for the full lifecycle of data at massive scale. In this episode Flavio Villanustre, VP of infrastructure and products at HPCC Systems, shares the history of the platform, how it is architected for scale and speed, and the unique solutions that it provides for enterprise grade data analytics. He also discusses the motivations for open sourcing the platform, the detailed workflow that it enables, and how you can try it for your own projects. This was an interesting view of how a well engineered product can survive massive evolutionary shifts in the industry while remaining relevant and useful.
The extract and load pattern of data replication is the most commonly needed process in data engineering workflows. Because of the myriad sources and destinations that are available, it is also among the most difficult tasks that we encounter. Fivetran is a platform that does the hard work for you and replicates information from your source systems into whichever data warehouse you use. In this episode CEO and co-founder George Fraser explains how it is built, how it got started, and the challenges that creep in at the edges when dealing with so many disparate systems that need to be made to work together. This is a great conversation to listen to for a better understanding of the challenges inherent in synchronizing your data.
Data is only valuable if you use it for something, and the first step is knowing that it is available. As organizations grow and data sources proliferate it becomes difficult to keep track of everything, particularly for analysts and data scientists who are not involved with the collection and management of that information. Lyft has build the Amundsen platform to address the problem of data discovery and in this episode Tao Feng and Mark Grover explain how it works, why they built it, and how it has impacted the workflow of data professionals in their organization. If you are struggling to realize the value of your information because you don't know what you have or where it is then give this a listen and then try out Amundsen for yourself.
The ETL pattern that has become commonplace for integrating data from multiple sources has proven useful, but complex to maintain. For a small number of sources it is a tractable problem, but as the overall complexity of the data ecosystem continues to expand it may be time to identify new ways to tame the deluge of information. In this episode Tim Ward, CEO of CluedIn, explains the idea of eventual connectivity as a new paradigm for data integration. Rather than manually defining all of the mappings ahead of time, we can rely on the power of graph databases and some strategic metadata to allow connections to occur as the data becomes available. If you are struggling to maintain a tangle of data pipelines then you might find some new ideas for reducing your workload.
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Comments (1)

T L

It's very hard to follow your guest..

Sep 22nd
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