OneLake Security + Shortcuts: The RLS Architecture That Actually Holds Up in Production
You configure row-level security on your gold Lakehouse. You test it rows filter correctly. You ship it. Two weeks later, another team creates a shortcut from their workspace and discovers they see every row. This isn't a Fabric bug. It's the consequence of conflating Power BI RLS, SQL endpoint security policies, and OneLake Security and assuming they propagate through shortcuts the same way. They don't. This article is the reference I wish I'd had.Direct Lake Is Changing the Lakehouse vs Warehouse Debate in Microsoft Fabric
Most Fabric discussions still focus on Lakehouse versus Warehouse. I believe that's increasingly the wrong question. Thanks to Direct Lake, many organizations can now go directly from Lakehouse to Power BI without introducing a Warehouse layer. But there are important trade-offs and hidden performance considerations that every Fabric architect should understand before making that choice. Let's dive into what really drives the decision.
From Days to Minutes:Revolutionising Fabric Data Agent Creation and fine tuning using VScode Plugins
A Big Boost in Productivity Data engineering is changing fast. Earlier, setting up a Fabric Data Agent meant spending 30 to 60 minutes clicking through portals and doing repetitive manual work. With AgentForge, this has changed completely. AgentForge brings Fabric Data Agent setup into VS Code, using natural language powered by the Model Context Protocol (MCP). What once took nearly an hour can now be done in just 2 minutes for most agents. Even complex agents that work with large repositories are ready in about 4 minutes. This is not just a small improvement—it’s a major shift from manual clicks to AI-driven workflows that save time and reduce mistakes.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.
