Data Mesh Architecture: A Modern Distributed Data Model | Streaming Audio: Apache Kafka® & Real-Time Data podcast

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Episode description

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Episode description

Data mesh isn’t software you can download and install, so how do you build a data mesh? In this episode, Adam Bellemare (Staff Technologist, Office of the CTO, Confluent) discusses his data mesh proof of concept and how it can help you conceptualize the ways in which implementing a data mesh could benefit your organization.

Adam begins by noting that while data mesh is a type of modern data architecture, it is only partially a technical issue. For instance, it encompasses the best way to enable various data sets to be stored and made accessible to other teams in a distributed organization. Equally, it’s also a social issue—getting the various teams in an organization to commit to publishing high-quality versions of their data and making them widely available to everyone else. Adam explains that the four data mesh concepts themselves provide the language needed to start discussing the necessary social transitions that must take place within a company to bring about a better, more effective, and efficient data strategy.

The data mesh proof of concept created by Adam's team showcases the possibilities of an event-stream based data mesh in a fully functional model. He explains that there is no widely accepted way to do data mesh, so it's necessarily opinionated. The proof of concept demonstrates what self-service data discovery looks like—you can see schemas, data owners, SLAs, and data quality for each data product. You can also model an app consuming data products, as well as publish your own data products.

In addition to discussing data mesh concepts and the proof of concept, Adam also shares some experiences with organizational data he had as a staff data platform engineer at Shopify. His primary focus was getting their main ecommerce data into Apache Kafka® topics from sharded MySQL—using Kafka Connect and Debezium. He describes how he really came to appreciate the flexibility of having access to important business data within Kafka topics. This allowed people to experiment with new data combinations, letting them come up with new products, novel solutions, and different ways of looking at problems. Such data sharing and experimentation certainly lie at the heart of data mesh.

Adam has been working in the data space for over a decade, with experience in big-data architecture, event-driven microservices, and streaming data platforms. He’s also the author of the book “Building Event-Driven Microservices.”

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