Build A Common Understanding Of Your Data Reliability Rules With Soda Core and Soda Checks Language

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 When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. 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You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline category on review platforms like G2. Go to dataengineeringpodcast.com/hevodata and sign up for a free 14-day trial that also comes with 24×7 support. 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 LinkedIn @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. 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 [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Soda Data Podcast Episode Soda Checks Language Great Expectations Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast