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.Microsoft Fabric: A Data Engineer's Perspective. What I Learned Building Real Pipelines
After months of building production-grade data workflows on Microsoft Fabric, I share what genuinely works, what requires workarounds, and where the platform is heading from ingestion to transformation to serving.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: LabBuilding a Scalable Learning Analytics Architecture: Integrating Moodle LMS with Microsoft Fabric
This article explains how Microsoft Fabric integrates with Moodle LMS REST API to create a scalable and reliable learning analytics ecosystem. We will walk through API integration, ingestion, lakehouse storage, Spark optimization, and automated pipelines: the foundation required to operationalize LMS analytics at an enterprise level.
