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Tackling Real Time Streaming Data With SQL Using RisingWave

Tackling Real Time Streaming Data With SQL Using RisingWave

FromData Engineering Podcast


Tackling Real Time Streaming Data With SQL Using RisingWave

FromData Engineering Podcast

ratings:
Length:
57 minutes
Released:
Feb 4, 2024
Format:
Podcast episode

Description

Summary
Stream processing systems have long been built with a code-first design, adding SQL as a layer on top of the existing framework. RisingWave is a database engine that was created specifically for stream processing, with S3 as the storage layer. In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable.
Announcements
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Your host is Tobias Macey and today I'm interviewing Yingjun Wu about the RisingWave database and the intricacies of building a stream processing engine on S3
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what RisingWave is and the story behind it?
There are numerous stream processing engines, near-real-time database engines, streaming SQL systems, etc. What is the specific niche that RisingWave addresses?
What are some of the platforms/architectures that teams are replacing with RisingWave?
What are some of the unique capabilities/use cases that RisingWave provides over other offerings in the current ecosystem?
Can you describe how RisingWave is architected and implemented?
How have the design and goals/scope changed since you first started working on it?
What are the core design philosophies that you rely on to prioritize the ongoing development of the project?
What are the most complex engineering challenges that you have had to address in the creation of RisingWave?
Can you describe a typical workflow for teams that are building on top of RisingWave?
What are the user/developer experience elements that you have prioritized most highly?
What are the situations where RisingWave can/should be a system of record vs. a point-in-time view of data in transit, with a data warehouse/lakehouse as the longitudinal storage and query engine?
What are the most interesting, innovative, or unexpected ways that you have seen RisingWave used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on RisingWave?
When is RisingWave the wrong choice?
What do you have planned for the future of RisingWave?
Contact Info
yingjunwu (https://github.com/yingjunwu) on GitHub
Personal Website (https://yingjunwu.github.io/)
LinkedIn (https://www.linkedin.com/in/yingjun-wu-4b584536/)
Parting Question
From your
Released:
Feb 4, 2024
Format:
Podcast episode

Titles in the series (100)

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