
Abstract: Data Mesh is the big new paradigm in data architecture, like microservices for data. Let's break through the hype and see why it's catching on, what's involved in building a mesh implementation and what tech is involved.
We'll first introduce data mesh and see exactly how it's like microservices for data. We'll understand the key mesh concept of decentralised data products. We'll see what a data product looks like, how they vary and how they hang together in a mesh with central discoverability and federated governance. Through this we'll get a feel for the range of mesh use cases. We'll then dive into what tech is available for implementing mesh architectures. We'll see how cloud providers and commercial data platforms can help and what specific types of tools are available for fulfilling data mesh capabilities.
Bio: Ryan Dawson is a technologist passionate about data. Ryan works with clients on large-scale data and AI initiatives, helping organizations get more value from data. His work includes strategies to productionize machine learning, organizing the way data is captured and shared, selecting the right data technologies and optimal team structures, as well as writing the code to make it happen. He has over 15 years of experience and, as well as many widely read articles about MLOps, software design, and delivery. is author of the Thoughtworks Guide to Evaluating MLOps Platforms.

Ryan Dawson
Title
Principal Data Engineer | Thoughtworks
