Data mesh is an exciting new approach to designing and developing data architectures. After my first series of articles and more recent materials on data products and data domains, I received queries on explaining what a practical implementation on Azure could look like. I’ll try to answer this question in…

Data mesh in practice — All clients I work with are either interested in or planning their next generation of a modern data platform. In this transition, data mesh architecture is gaining a lot of traction. At the same time there are concerns about interpreting this concept: practical guidance is missing. About data mesh Data mesh advocates treating…

Strategic path towards designing an enterprise-scale data mesh — In all data mesh discussions I have with enterprises the subject of data domains draws most attention. Customers find domain-oriented data ownership the hardest part of data mesh. Often, there is a fundamental lack of understanding of Domain-Driven Design (DDD). Other data practitioners find the conceptual notions of DDD too…

Design considerations for building a data mesh architecture — All organizations I work with understand the importance of data and are either interested in or planning their next generation of a modern data platform. Their aim is to move away from tightly coupled data interfaces and varying data flows towards an architecture that allows eco-system connectivity: a cloud distributed…

One of my customers asked me a question: what is your view on features stores? And how to position them in the data mesh paradigm? My view: I’m no big fan of centralized or shared feature stores. They add unnecessary complexity to your architecture. Each individual analytical use case is…

In the blogpost Modern Data Pipelines, you learned about data pipelines and some of their difficulties. You also read up on an approach by binding Azure Synapse Analytics and Azure Purview in order to build a framework for intelligently processing data. In this article I want to take the data…

In a federated architecture, in which responsibilities are distributed between domains, it’s harder to oversee dependencies and obtain insights in data usage. This is where data contracts come into play. Why do data contracts matter? Because they provide insights into who owns what data products. They support setting standards and…

Nuances and considerations for implementing data mesh — A data mesh architecture distributes data ownership across domain teams, with federated governance and decentralized data products. Data mesh contrasts from other data architectures because it’s highly decentralized: distributed as opposed to centralized. Observations Many large enterprises that I talk to study data mesh. They find the scaling model of teams…

If it’s stable and it truly matters, consider using MDM — Enterprises using a Data Mesh Architecture typically have a large multitude of domains, each with its own systems and data. This means increased complexity because the data is spread around and multiple versions of the same data might exist. Integration, for example providing a 360-degree view of your customers, consequently…