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Data Mesh

Articles, podcasts, talks, and more about Data Mesh.

The Data Dilemma, Part 2

The first part showed why agents fail on undocumented data semantics and why Data Mesh is not a realistic answer for many mid-sized companies. This second part gets concrete: a Minimum Viable Data Mesh in four layers, from an honest inventory through curated views with ODCS contracts to agent access via MCP. A purchasing agent that compares suppliers and evaluates price histories serves as a running example.

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The Data Dilemma, Part 1

When AI agents fail in a company, it is rarely the model’s fault. The real problem is the data: semantics, quality, and context are written down nowhere, they live in the heads of the domain experts. Data Mesh addresses exactly this problem, but it organizationally overwhelms many mid-sized companies. This first part describes why that is and derives a Minimum Viable Data Mesh from it. The second part shows the implementation in four layers.

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The Missing Half of Your Data Strategy

Why Data Literacy Makes or Breaks Data Product Oriented Architectures

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The right size of a Data Product

Setting the boundaries of data products incorrectly can lead to integration issues, unclear ownership and duplicated logic. This guide offers practical heuristics for creating data products of the right size.

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How To Build a Data Product with Databricks

In today’s data engineering, the focus is primarily on developing modular data products. This article outlines the advantages of modularity over monolithic data pipelines and explains, step-by-step, how to develop data products using Databricks – from defining a data contract to creating and implementing Databricks Asset Bundles, setting up a CI/CD pipeline, and publishing metadata.

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What is a Data Product?

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Does BI Software Make Sense for Development Teams?

Time is a valuable asset, and it is easily consumed by the analysis and preparation of data. In other words, there may be good reason to come to grips with BI software.

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Why Your Team Needs Data Products

Have you ever had the feeling that you or your team spends too much time on individual data research tasks or makes too many decisions based on your gut? Then it might be time for your team to start engaging with data products.

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Creating data products with Terraform on AWS

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Processing medical study data with Data Mesh technologies

Together with our customer CluePoints, we evaluated new technologies, tools and standards for data storage, data processing, data versioning, and data lineage. These might become useful for refactoring their self-serve data platform.