Summary

Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. To address this shortcoming Datorios created an observability platform for Flink that brings visibility to the internals of this popular stream processing system. In this episode Ronen Korman and Stav Elkayam discuss how the increased understanding provided by purpose built observability improves the usefulness of Flink.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Ronen Korman and Stav Elkayam about pulling back the curtain on your real-time data streams by bringing intuitive observability to Flink streams

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Datorios is and the story behind it?
Data observability has been gaining adoption for a number of years now, with a large focus on data warehouses. What are some of the unique challenges posed by Flink?
- How much of the complexity is due to the nature of streaming data vs. the architectural realities of Flink?
How has the lack of visibility into the flow of data in Flink impacted the ways that teams think about where/when/how to apply it?
How have the requirements of generative AI shifted the demand for streaming data systems?
- What role does Flink play in the architecture of generative AI systems?
Can you describe how Datorios is implemented?
- How has the design and goals of Datorios changed since you first started working on it?
How much of the Datorios architecture and functionality is specific to Flink and how are you thinking about its potential application to other streaming platforms?
Can you describe how Datorios is used in a day-to-day workflow for someone building streaming applications on Flink?
What are the most interesting, innovative, or unexpected ways that you have seen Datorios used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datorios?
When is Datorios the wrong choice?
What do you have planned for the future of Datorios?

Contact Info

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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X-Ray Vision For Your Flink Stream Processing With Datorios 42:22 Share

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X-Ray Vision For Your Flink Stream Processing With Datorios