Build A Common Understanding Of Your Data Reliability Rules With Soda Core and Soda Checks Language
Data Engineering Podcast
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Resolving the Data Issues
In order to get reliable software, there's two main ingredients that are quite common right now: test sweets and observability. So if you want to keep releasing softer with confidence, you need good, automated test sweets. If you want to be able to diagnose your issues in production, you need observeability tools, like a data dog or a new relic. And the same is true for data. So data testing brings the issues to the surface, makes sure you are the first to know. That's the world i came out before, so it's good to see that these principles are now taking on as well.
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Summary
Regardless of how data is being used, it is critical that the information is trusted. The practice of data reliability engineering has gained momentum recently to address that question. To help support the efforts of data teams the folks at Soda Data created the Soda Checks Language and the corresponding Soda Core utility that acts on this new DSL. In this episode Tom Baeyens explains their reasons for creating a new syntax for expressing and validating checks for data assets and processes, as well as how to incorporate it into your own projects.
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 Tom Baeyens about Soda Data’s new DSL for data reliability
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what SodaCL is and the story behind it?
- What is the scope of functionality that SodaCL is intended to address?
- What are the ways that reliability is measured for data assets? (what is the equivalent to site uptime?)
- What are the core abstractions that you identified for simplifying the declaration of data validations?
- How did you approach the design of the SodaCL syntax to balance flexibility for various use cases, with structure and opinionated application?
- Why YAML?
- Can you describe how the Soda Core utility is implemented?
- How have the design and scope of the SodaCL dialect and the Soda Core framework evolved since you started working on them?
- What are the available integration/extension points for teams who are using Soda Core?
- Can you describe how SodaCL integrates into the workflow of data and analytics engineers?
- What is your process for evolving the SodaCL dialect in a maintainable and sustainable manner?
- What are the most interesting, innovative, or unexpected ways that you have seen SodaCL used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on SodaCL?
- When is SodaCL the wrong choice?
- What do you have planned for the future of SodaCL?
Contact Info
- @tombaeyens on Twitter
- tombaeyens on GitHub
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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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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