Data Engineering Podcast

Bring Your Code To Your Streaming And Static Data Without Effort With The Deephaven Real Time Query Engine

Feb 14, 2022

01:02:05

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Highlights

AI Chapters

Episode notes

Deephaven

03:48

Deephaven Engine - Streaming Tables

04:23

Machine Learning - What's the Biggest Challenge?

02:32

Introduction

5min

Deephaven vs Materializes Up Date Model

2min

What Motivated You to Extend Your Hedge Fund Business?

5min

Deephaven Engine - Streaming Tables

4min

Deephaven and the Data Scientist Persona

5min

Deep Haven Is a Python Application, and It's a Python First Experience

4min

Deephaven

4min

Machine Learning - What's the Biggest Challenge?

3min

Data Pipe Lines - A New Way to Communicate Dynamic Cables

5min

10.

CSV Parser in Java

3min

11.

The Brazer Interaction Is a Very Good Idea

4min

12.

What's the Long Term Sustainability of the Ecosystem?

5min

13.

Iot Crypto Block Chain Is a Great Way to Build a Real Time Cryptocurrency Block Chain

2min

14.

We've Learned a Lot Along the Way

3min

15.

Is Deephaven the Right Choice?

6min

16.

Deephaven - What's the Biggest Gap in the Tooling for Data Management?

3min

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
LinkedIn

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.
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Links

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

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