Making Sense Of The Technical And Organizational Considerations Of Data Contracts
Data Engineering Podcast
00:00
The Challenges of Data Quality
There are a number of interesting challenges around that as far as where to employ the enforcement. One lens just to put on that is I think this is the problem of trying to apply types without a compiler and so your compilation has to be social, right? It's people working out things among themselves. Downstream when you get to the business users, there's often more friction there because they don't know how hard it is to guarantee data quality. And some of the teams where you kind of see these kinds of changes happening are places where you have like really strong, assertive, forward looking data leaders.
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Summary
One of the reasons that data work is so challenging is because no single person or team owns the entire process. This introduces friction in the process of collecting, processing, and using data. In order to reduce the potential for broken pipelines some teams have started to adopt the idea of data contracts. In this episode Abe Gong brings his experiences with the Great Expectations project and community to discuss the technical and organizational considerations involved in implementing these constraints to your data workflows.
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 Abe Gong about the technical and organizational implementation of data contracts
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what your conception of a data contract is?
- What are some of the ways that you have seen them implemented?
- How has your work on Great Expectations influenced your thinking on the strategic and tactical aspects of adopting/implementing data contracts in a given team/organization?
- What does the negotiation process look like for identifying what needs to be included in a contract?
- What are the interfaces/integration points where data contracts are most useful/necessary?
- What are the discussions that need to happen when deciding when/whether a contract "violation" is a blocking action vs. issuing a notification?
- At what level of detail/granularity are contracts most helpful?
- At the technical level, what does the implementation/integration/deployment of a contract look like?
- What are the most interesting, innovative, or unexpected ways that you have seen data contracts used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data contracts/great expectations?
- When are data contracts the wrong choice?
- What do you have planned for the future of data contracts in great expectations?
Contact Info
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
- Great Expectations
- Progressive Typing
- Pioneers, Settlers, Town Planners
- Pydantic
- Typescript
- Duck Typing
- Flyte
- Dagster
- Trino
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
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