Streaming Data Pipelines Made SQL With Decodable

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October 28th, 2021

1 hr 9 mins 32 secs

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About this Episode

Summary

Streaming data systems have been growing more capable and flexible over the past few years. Despite this, it is still challenging to build reliable pipelines for stream processing. In this episode Eric Sammer discusses the shortcomings of the current set of streaming engines and how they force engineers to work at an extremely low level of abstraction. He also explains why he started Decodable to address that limitation and the work that he and his team have done to let data engineers build streaming pipelines entirely in SQL.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
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  • Your host is Tobias Macey and today I’m interviewing Eric Sammer about Decodable, a platform for simplifying the work of building real-time data pipelines

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Decodable is and the story behind it?
  • Who are the target users, and how has that focus informed your prioritization of features at launch?
  • What are the complexities that data engineers encounter when building pipelines on streaming systems?
  • What are the distributed systems concepts and design optimizations that are often skipped over or misunderstood by engineers who are using them? (e.g. backpressure, exactly once semantics, isolation levels, etc.)
    • How do those mismatches in understanding and expectation impact the correctness and reliability of the workflows that they are building?
  • Can you describe how you have architected the Decodable platform?
    • What have been the most complex or time consuming engineering challenges that you have dealt with so far?
  • What are the points of integration that you expose for engineers to wire in their existing infrastructure and data systems?
  • What has been your process for designing the interfaces and abstractions that you are exposing to end users?
    • What are some of the leaks in those abstractions that have either started to show or are anticipated?
  • What have you learned about the state of data engineering and the costs and benefits of real-time data while working on Decodable?
  • What are the most interesting, innovative, or unexpected ways that you have seen Decodable used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Decodable?
  • When is Decodable the wrong choice?
  • What do you have planned for the future of Decodable?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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