Unlocking the Power of Metadata-Driven Data Pipelines in Microsoft Fabric

Are you managing dozens or even hundreds of dataflows and tasks in your data warehousing pipelines? If you're aiming to elevate your Data Analytic platform to a Modern Data Estate, a metadata-driven approach can be a game-changer. This method can significantly reduce ETL development effort and simplify your data pipelines.

In this session, you'll learn how to build efficient pipelines in Microsoft Fabric, driven by metadata stored in JSON files within OneLake—a Delta Lakehouse designed for all your analytic workloads. These patterns can dramatically boost your ETL development velocity and are applicable across any analytics environment.

Once you've created a pipeline for a single table load, scaling up to handle dozens or even hundreds of tables becomes straightforward—simply by adding metadata to configuration files.

We'll explore essential patterns for:

· Managing dependencies (e.g., loading specific tables before others)

· Implementing incremental loading (e.g., loading only data that has changed since the last execution)

· Parameterizing various options to enhance flexibility

By mastering these patterns, you can minimize coding time for ETL processes and significantly increase your productivity.

Join us to discover how you can transform your data pipelines and achieve greater efficiency in your analytics workflows.