Data Engineering Podcast
Build A Common Understanding Of Your Data Reliability Rules With Soda Core and Soda Checks Language
Podcast summary created with Snipd AI
Quick takeaways
- Data reliability engineering is crucial for data trust and quality.
- Soda Core emphasizes identifying and fixing root causes of data issues.
- Soda Checks Language provides structured testing and observability for enhanced data reliability.
Deep dives
Overview of Atlan's Active Metadata Capabilities
Atlan's active metadata platform transforms data ecosystems by enhancing data freshness and quality for business intelligence. It automates warehouse scaling based on usage patterns, allowing human focus on value delivery. Pioneering data teams like Postman and WeWork benefit from Atlan.
Soda Checks Language's Role in Data Reliability
Soda Data's DSL focuses on data quality, born from a need to describe good data accurately. The language supports the overall approach to ensure data continuity and trust. It helps find data issues effectively and emphasizes the importance of signal-to-noise ratio for quality alerts.
Soda Core Engine's Approach to Data Issue Resolution
Soda Core features a systematic approach to resolving data issues by identifying and fixing root causes. It emphasizes collaboration between teams since most data issues originate within production systems. The engine's emphasis on reliability reflects principles of software engineering.
Expansion of Soda Checks Language and Core Engine
The soda checks language and core engine provide automated testing and observability to enhance data reliability. The extensible nature caters to community feedback, supporting use cases that include monitoring business metrics, enhancing trust in data assets, and managing data quality effectively.
Soda Checks Language's Agile Project Management
The language offers clear structuring and grouping of checks for easy comprehension and management. Engineers and analysts can work collaboratively, defining checks at a file level for clear ownership. The language's usability evolves to adapt to varying data team sizes and complexity levels.
Future Direction of Soda Checks Language and Core Engine
The project anticipates enhancing the authoring experience with enhanced support tools like check suggestions and live templates. Focus remains on continuous community engagement and self-service capabilities through Soda Cloud for efficient data quality management.
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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- Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos.
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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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