An Overview of Lakehouses and Data Warehouses
Unlocking the Future of Data: Lakehouses vs. Data Warehouses In today’s data-driven world, choosing the right architecture is crucial for turning information into insight. Are traditional data warehouses still the gold standard, or are modern lakehouses rewriting the rules? Dive into our latest article as we explore how these two powerful approaches stack up — and discover which one could transform your data strategy.Microsoft Fabric Data Warehouse
Microsoft Fabric Data Warehouse (Azure Synapse) Microsoft Fabric introduces a new era in data management, and at its core lies a transformative capability—the Data Warehouse, also known as Azure Synapse within Fabric. While it builds on familiar concepts from traditional BI systems, Fabric’s Data Warehouse breaks new ground by offering a truly open, scalable, and fully integrated analytical environment. This article offers a high-level overview of Microsoft Fabric Data Warehouse, providing foundational insights for professionals ready to embrace modern data architecture.
# Top 10 Anti-Patterns in Fabric Warehouse Production Code Part -2
You fixed the loops, batched the loads, and listed your columns explicitly. Good. But your Fabric Warehouse still has hidden cost leaks. In Part 2, we tackle the six anti-patterns that don't show up until your tables are large and your team is shipping fast: scalar UDFs that silently dodge Fabric's inlining engine, MERGE statements that join on the wrong column (and why case-sensitivity makes it worse), small-file fragmentation from over-partitioned lakehouse shortcuts — and the OPTIMIZE + VACUUM + V-Order trio that fixes it — plus three CI/CD habits (missing column lists, hardcoded workspace GUIDs, and DROP-recreate cycles) that quietly erase your statistics, your permissions, and your Monday morning peace of mind. Ten anti-patterns. Two posts. One rule: Fabric Warehouse is not SQL Server.
