Latest Fabric / Data Content — Page 2 — Tutorials

Managing historical changes in dimensional tables is crucial when extracting value from data, and Slowly Changing Dimension (SCD) patterns provide the framework for handling these changes.

This deep dive explains how to operationalize Python-based ML pipelines in Microsoft Fabric, from notebooks to wheel packages and pipeline automation.

Ever wished you could have a team of specialized advisors at your fingertips, each ready to answer your toughest data…

Microsoft Fabric simplifies Python package management with managed, layered environments and tooling to detect dependency conflicts. Learn best practices for pinning versions, isolating projects, and using uv in a practical Fabric workflow.

Microsoft’s acquisition of Osmos signals a major push toward automating data engineering inside Microsoft Fabric. Expect faster ingestion, smarter cleaning, and no-code pipelines that broaden access to analytics.

Translytical Task Flows in Microsoft Fabric unify transactions and analytics in one pipeline. They deliver real-time insights and automated actions securely.

Pablo Moreno details the impacts of lakehouse schemas being generally available inside of Microsoft Fabric.