Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable

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October 15th, 2023

1 hr 8 mins 28 secs

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About this Episode

Summary

Building streaming applications has gotten substantially easier over the past several years. Despite this, it is still operationally challenging to deploy and maintain your own stream processing infrastructure. Decodable was built with a mission of eliminating all of the painful aspects of developing and deploying stream processing systems for engineering teams. In this episode Eric Sammer discusses why more companies are including real-time capabilities in their products and the ways that Decodable makes it faster and easier.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
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  • You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free!
  • As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES.
  • Your host is Tobias Macey and today I'm interviewing Eric Sammer about starting your stream processing journey with Decodable

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Decodable is and the story behind it?
    • What are the notable changes to the Decodable platform since we last spoke? (October 2021)
    • What are the industry shifts that have influenced the product direction?
  • What are the problems that customers are trying to solve when they come to Decodable?
  • When you launched your focus was on SQL transformations of streaming data. What was the process for adding full Java support in addition to SQL?
  • What are the developer experience challenges that are particular to working with streaming data?
    • How have you worked to address that in the Decodable platform and interfaces?
  • As you evolve the technical and product direction, what is your heuristic for balancing the unification of interfaces and system integration against the ability to swap different components or interfaces as new technologies are introduced?
  • 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?

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Parting Question

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

Closing Announcements

  • Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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