Build Confidence In Your Data Platform With Schema Compatibility Reports That Span Systems And Domains Using Schemata
Data Engineering Podcast
Scamata - What's the Difference Between Avro and Avro?
Schema has its own internal schema representation. You can build multiple apps on top of it. It's a very connected ecosystem. The flow goes like a protocol provider, pass the protocol and convert that into a internalscheme representation. And then you can build your own app or we can have your own customized app here. Schema.score is one of the apps that compute the score and then give it to you.
Summary
Data engineering systems are complex and interconnected with myriad and often opaque chains of dependencies. As they scale, the problems of visibility and dependency management can increase at an exponential rate. In order to turn this into a tractable problem one approach is to define and enforce contracts between producers and consumers of data. Ananth Packildurai created Schemata as a way to make the creation of schema contracts a lightweight process, allowing the dependency chains to be constructed and evolved iteratively and integrating validation of changes into standard delivery systems. In this episode he shares the design of the project and how it fits into your development practices.
Announcements
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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 Ananth Packkildurai about Schemata, a modelling framework for decentralised domain-driven ownership of data.
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Schemata is and the story behind it?
- How does the garbage in/garbage out problem manifest in data warehouse/data lake environments?
- What are the different places in a data system that schema definitions need to be established?
- What are the different ways that schema management gets complicated across those various points of interaction?
- Can you walk me through the end-to-end flow of how Schemata integrates with engineering practices across an organization’s data lifecycle?
- How does the use of Schemata help with capturing and propagating context that would otherwise be lost or siloed?
- How is the Schemata utility implemented?
- What are some of the design and scope questions that you had to work through while developing Schemata?
- What is the broad vision that you have for Schemata and its impact on data practices?
- How are you balancing the need for flexibility/adaptability with the desire for ease of adoption and quick wins?
- The core of the utility is the generation of structured messages How are those messages propagated, stored, and analyzed?
- What are the pieces of Schemata and its usage that are still undefined?
- What are the most interesting, innovative, or unexpected ways that you have seen Schemata used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Schemata?
- When is Schemata the wrong choice?
- What do you have planned for the future of Schemata?
Contact Info
- ananthdurai on GitHub
- @ananthdurai on Twitter
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
- Schemata
- Data Engineering Weekly
- Zendesk
- Ralph Kimball
- Data Warehouse Toolkit
- Iteratively
- Protocol Buffers (protobuf)
- Application Tracing
- OpenTelemetry
- Django
- Spring Framework
- Dependency Injection
- JSON Schema
- dbt
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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