Choosing the Right Way to Run Python in Microsoft Fabric
Fabric gives us several ways to run Python, and at first they can look overlapping. In this post, I share the practical decision model I use to choose the right option based on execution mode, compute engine, and data access path. If you are code-first and want fewer wrong turns when moving from exploration to production, this guide is for you.Lakehouse in Microsoft Fabric: One Platform, Many Analytics Possibilities
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: LabAutomate SharePoint to Fabric Lakehouse Data Sync with Python
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.Direct Lake: Faster Power BI, No Refreshes, Seamless Fabric Integration
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-overviewMicrosoft Fabric Lakehouses: The Engine Room of Data Engineering
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.Data Engineering with Microsoft Fabric: Efficiently Loading Data into a Lakehouse
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
