Summary

To process your data you need to know what shape it has, which is why schemas are important. When you are processing that data in multiple systems it can be difficult to ensure that they all have an accurate representation of that schema, which is why Confluent has built a schema registry that plugs into Kafka. In this episode Ewen Cheslack-Postava explains what the schema registry is, how it can be used, and how they built it. He also discusses how it can be extended for other deployment targets and use cases, and additional features that are planned for future releases.

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Preamble

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Your host is Tobias Macey and today I’m interviewing Ewen Cheslack-Postava about the Confluent Schema Registry

Interview

Introduction

How did you get involved in the area of data engineering?

What is the schema registry and what was the motivating factor for building it?

If you are using Avro, what benefits does the schema registry provide over and above the capabilities of Avro’s built in schemas?

How did you settle on Avro as the format to support and what would be involved in expanding that support to other serialization options?

Conversely, what would be involved in using a storage backend other than Kafka?

What are some of the alternative technologies available for people who aren’t using Kafka in their infrastructure?

What are some of the biggest challenges that you faced while designing and building the schema registry?

What is the tipping point in terms of system scale or complexity when it makes sense to invest in a shared schema registry and what are the alternatives for smaller organizations?

What are some of the features or enhancements that you have in mind for future work?

Contact Info

ewencp on GitHub

Website

@ewencp on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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