What the Heck is a Data Mesh?!
I got sucked into a data mesh Twitter thread this weekend (it’s worth a read if you haven’t seen it). Data meshes have clearly struck a nerve. Some don’t understand them, while others believe they’re a bad idea. Yet, “Demystifying Data Mesh” and “Putting Data Mesh to Work” articles abound.
To understand the confusion, I re-read Zhamak Dehghani ’s original and follow-on posts. Zhamak is the creator of the data mesh. In her second post she identifies four data mesh principles:
I believe that putting these principles on equal footing creates confusion. The second principle—data as a product—is the motivating reason for a data mesh and worth considering on its own. Once we explore what “data as a product” is, we can discuss the implications: the need for decentralization, self-serve infrastructure, and federated governance.
Application data has customers just like any other product. Data scientists, business analysts, finance, sales operations, product managers, and engineers all use application data. Machine learning models, charts, graphs, reports, and even other web services are all built on top of application data. And application data is being increasingly exposed to paying customers as part of the “real” product (Stripe’s Sigma is an example).
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