Joe Reis Talks Data Engineering's Past, Present, and Future - Part 1 | Ep. 36 | RTA Podcast

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  • เผยแพร่เมื่อ 15 ม.ค. 2024
  • Follow: stree.ai/podcast | Sub: stree.ai/sub | New episodes every Monday! In this week's episode, Tim chats with Joe Reis, a seasoned expert in data engineering and co-author of "Fundamentals of Data Engineering." They delve into the evolution of data engineering from ETL, the role of real-time data and analytics, and the future trajectory of the field. Joe also shares his diverse experiences, from being a 'recovering data scientist' to his current focus as a content creator and consultant.
    ► The Fundamentals of Data Engineering by Joe Reis and Matt Housley: www.oreilly.com/library/view/...
    ► The Joe Reis Show: open.spotify.com/show/3mcKitY...
    ► Monday Morning Data Chat: podcasts.apple.com/us/podcast...
    🎧Check out our new limited series podcast, "Keyboard and Quill, " which explores data and technology through time: stree.ai/keyboardandquill
    ABOUT THE PODCAST
    From StarTree, founded by the creators of Apache Pinot™, "Real-Time Analytics with Tim Berglund" is a podcast dedicated to bringing analytics from the dashboard to the user interface. Accessible but technically rich, the show focuses on the infrastructure, tools, and techniques being developed by the people building systems that are serving analytics to our users in real-time. New episodes every Monday. Follow on Spotify, Apple, Google, etc at stree.ai/podcast
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    ABOUT STARTREE
    StarTree, powered by Apache Pinot: real-time analytics for user-facing apps. Discover the latest technical developments, trends, and use cases for real-time analytics straight from our engineers and open source developers, and your fellow industry colleagues and practitioners.
    StarTree is uniquely positioned to bring you fast, reliable and fresh perspectives on the shift from batch to real-time analytics, and from internal-facing to user-facing applications. Subscribe to our channel if you are designing systems that need to scale to petabytes of information, millions of users, and to provide results at sub-second scale latencies.
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