A practical view on how metadata-driven automation meets measurable business value. Financial data has a way of exposing every weakness in our data-quality setup. In many domains, imperfect data is tolerated — an empty description here, a wrong code there. But in finance, a single invalid record can flip a balance sheet, distort a P&L, or derail a budget review. As Piotr Czarnas of DQOps observes: “Business users can accept imperfect data — until it costs them money.” When...

Track A (Business) — Part 3 of the Entity‑First series Series navigation: A1 — Why We Need a Business‑Friendly Data Transformation Model • A2 — Entities, Subject Areas & Transformation Steps • B1 — How dbt & SQLMesh Organize Metadata and Code • B2 — Designing an Entity‑First YAML → SQL Generator • A3 (you are here) This playbook turns the Entity‑First approach into day‑to‑day practice. It covers naming conventions, ownership/RACI, change workflow, and quality gates so that modelers and engineers ship reliable data products with the same rules,...

Most teams still “write” documentation by hand. But if you already have rich metadata in your modeling tools or databases, you can generate documentation — beautifully formatted, cross-linked, and always up to date. In this article I’ll show how to produce Word documents (.docx) with: 🔹A Table of Contents generated from headings 🔹Bookmarks on every entity (or any object you choose) 🔹Clickable cross-references between related objects 🔹Embedded lineage diagrams and other images …and then export those docs...

Connecting models, lineage, and platforms — making metadata both technical and understandable Metadata is the silent force behind every data platform. It defines models, controls lineage, drives automation, and ensures governance — yet it’s often invisible or too technical to understand. Business-Friendly Metadata explores how metadata can act as the operating system of the enterprise data platform — powering automation while staying interpretable by humans. It’s about designing metadata systems that integrate tools, translate meaning, and generate trust. This...

Over the past months, I’ve created a growing collection of Medium publications that explore the world of data modeling, metadata, architecture, and automation from two complementary perspectives — the technical and the human. This article explains how these publications connect, why the naming evolved, and how they now form a consistent ecosystem for readers and practitioners. Data architecture has many audiences. Some want to understand how models, metadata, and platforms work. Others need to see why these structures matter to business outcomes. When...

Bridging the gap between schemas and meaning to unlock model-driven data engineering. We’ve automated pipelines, scanned every schema, and still the business asks: “But what does this field mean?” I was once in a meeting where the data engineering team proudly unveiled a comprehensive data map. Every column from every system was listed. Lineage was traced through dozens of pipelines. Profiling stats were perfect. Then a marketing manager raised her hand. “Which column tells me if...

How data models evolved from descriptive blueprints to enforceable agreements between producers and consumers For decades, data modeling meant describing reality — identifying entities, defining relationships, and creating diagrams that visualized structure. But in today’s metadata-driven world, the model’s purpose has changed. A modern model is no longer just a representation of data — it’s a contract that encodes expectations, semantics, and quality boundaries. It doesn’t only describe what data looks like; it defines what must be true for data to...

Roelant Vos has posted a brief status update on what’s happening in the open source #datawarehouse #automation ecosystem! The generic interface for Data Warehouse Automation had to be re-based (recreated) in GitHub due to various issues around forking and branching. The repository has been re-created with the same name and is available at https://github.com/RoelantVos/Data_Warehouse_Automation_Metadata_Interface . TEAM is nearing completion for the v1.6 release. The following Kanban board shows what’s happening for v1.6: https://github.com/RoelantVos/TEAM/projects/1 . Virtual Data Warehouse v.1.6.1...

The upcoming Virtual Data Warehouse implementation & automation classroom training will be located in The Netherlands. Check out the details & register at www.virtualdwh.com. The training is delivered in conjunction with Doerffler & Partner and DCYSIVE and will once again focus on the design and implementation of a data platform that is truly flexible, scaleable and resilient. This is a very practical training that will provide the end-to-end overview of how to deliver a hybrid data warehouse solution. Utrecht is centrally...