Data mesh is a relatively recent term that describes a set of principles that good modern data systems uphold. A kind of “microservices” for the data-centric world. While the data mesh is not technology-specific as a pattern, the building of systems that adopt and implement data mesh principles have a relatively long history under different guises.
In this talk, we share our recommendations and picks of what every developer should know about building a streaming data mesh with Kafka. We introduce the four principles of the data mesh: domain-driven decentralization, data as a product, self-service data platform, and federated governance. We then cover topics such as the differences between working with event streams versus centralized approaches and highlight the key characteristics that make streams a great fit for implementing a mesh, such as their ability to capture both real-time and historical data. We’ll examine how to onboard data from existing systems into a mesh, modelling the communication within the mesh, how to deal with changes to your domain’s “public” data, give examples of global standards for governance, and discuss the importance of taking a product-centric view on data sources and the data sets they share.