Orchestration for Fabric notebooks

You have built a lakehouse in Microsoft Fabric. The notebooks are in place, data is flowing through the layers, and transformations are working. Now comes the next question: how do you orchestrate it all, reliably, scalably, and in a way that fits real-world workloads?

Should you use Data Factory pipelines, notebook-driven orchestration, materialized views, or bring in a third-party orchestration tool? Each option has clear strengths, but also trade-offs that can become painful in production if chosen incorrectly.

In this session, we take a deep, practical dive into the orchestration options available for Fabric lakehouses. We will break down how each approach actually works, where it excels, where it falls short, and, most importantly, when you should consider one over the others. Through concrete scenarios and production-proven examples, we will compare orchestration patterns for batch processing, dependencies, retries, monitoring, and operational complexity.