This article is based on official Microsoft Fabric documentation and practical learning resources provided by Microsoft. To move beyond theory and demonstrate real implementation, I also followed a hands-on Lakehouse lab published by Microsoft Learning. The lab walks through core concepts such as creating a lakehouse, ingesting data, and exploring it using different Fabric experiences.

If you would like to explore the same step-by-step exercise used in this article and in my demonstration, you can access the lab here:

Lab 

If you're working with files stored in SharePoint and need to regularly sync them to Microsoft Fabric Lakehouse, you have a few options. While Dataflow Gen2 provides a UI-driven approach for connecting to SharePoint data sources, it has limitations, it can't handle certain file types, may struggle with complex folder structures, and doesn't always support the flexibility needed for custom ETL logic.

What if you needed more control? A code-based solution that could download any file type from SharePoint, apply custom transformations, and load them into your Lakehouse with a single notebook run?

I've built an open-source PySpark notebook that does exactly that. In this post, I'll walk you through the solution, explain how it works, and show you how to get it running in your environment.

Spark JSON read and backfill optimizations

Microsoft Fabric revolutionizes data architecture by offering a unified platform that integrates Power BI, data science, real-time analytics, and more. At the heart of this ecosystem is the Lakehouse, a powerful, flexible, and scalable storage layer tailored for modern data engineering workflows.

In this article, we explore how Lakehouses work in Microsoft Fabric, how to set one up, and how they serve as the foundation for managing both files and structured data—all without the traditional complexity of data platforms.

Simplify analytics with Spark Connector for Microsoft Fabric Data Warehouse: seamlessly access Fabric Warehouse via a secure Spark API.

OneLake is a unified storage system in Microsoft Fabric that eliminates data silos by storing all data in a single location. Now, we’re going to discuss Direct Lake, a new way Power BI interacts with this storage for faster performance and efficiency.

Source: https://learn.microsoft.com/en-us/fabric/fundamentals/direct-lake-overview

A Guide to Working with Lakehouses in Microsoft Fabric

This guide explores the data engineering experience in Microsoft Fabric, focusing specifically on Lakehouses. This guide takes a hands-on approach, demonstrating how to work with Lakehouses and manage data effectively.

Downloadable Files