Bring Your Code To Your Streaming And Static Data Without Effort With The Deephaven Real Time Query Engine
Data Engineering Podcast
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Deephaven vs Materializes Up Date Model
deephaven was birth, let's say, a hedge fund that i founded. We started building thss in 20 12 cause we needed a data system that was good with both historical data and with real time data. The idea of our up date model is at an api level and at an engine level, is tracking ads, modified deletes and shifts such that many, many operations can be done in such a way that the incremental work to compute results is much, much smaller.
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Summary
Streaming data sources are becoming more widely available as tools to handle their storage and distribution mature. However it is still a challenge to analyze this data as it arrives, while supporting integration with static data in a unified syntax. Deephaven is a project that was designed from the ground up to offer an intuitive way for you to bring your code to your data, whether it is streaming or static without having to know which is which. In this episode Pete Goddard, founder and CEO of Deephaven shares his journey with the technology that powers the platform, how he and his team are pouring their energy into the community edition of the technology so that you can use it freely in your own work.
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 Pete Goddard about his work at Deephaven, a query engine optimized for manipulating and merging streaming and static data
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Deephaven is and the story behind it?
- What is the role of Deephaven in the context of an organization’s data platform?
- What are the upstream and downstream systems and teams that it is likely to be integrated with?
- Who are the target users of Deephaven and how does that influence the feature priorities and design of the platform?
- comparison of use cases/experience with Materialize
- What are the different components that comprise the suite of functionality in Deephaven?
- How have you architected the system?
- What are some of the ways that the goals/design of the platform have changed or evolved since you started working on it?
- What are some of the impedance mismatches that you have had to address between supporting different language environments and data access patterns? (e.g. batch/streaming/ML and Python/Java/R)
- Can you describe some common workflows that a data engineer might build with Deephaven?
- What are the avenues for collaboration across data roles and stakeholders?
- licensing choice/governance model
- What are the most interesting, innovative, or unexpected ways that you have seen Deephaven used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Deephaven?
- When is Deephaven the wrong choice?
- What do you have planned for the future of Deephaven?
Contact Info
- @pete_paco on Twitter
- @deephaven on Twitter
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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
- Deephaven
- Materialize
- Arrow Flight
- kSQLDB
- Redpanda
- Pandas
- NumPy
- Numba
- Barrage
- Debezium
- JPy
- Sabermetrics
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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