While working with Microsoft Fabric, a question came to mind: why use a Data Warehouse if the Lakehouse already provides a SQL endpoint? At first glance, it may seem redundant. However, when you look closer, the two serve very different purposes, and understanding these differences is key to knowing when to use each.
When organizations talk about becoming data-driven, the debate often comes down to where should data live and how should it be structured? That’s where the Data Lake and the Data Warehouse come into play. Both are critical, but their purposes and strengths differ.
Data becomes valuable when it’s turned into insights that drive action. In Microsoft Fabric, this is where Power BI shines. By connecting directly to Lakehouses and Warehouses in Fabric, you can build interactive dashboards and reports, then publish and share them securely across your organization.
As a data engineer, your job isn’t just about moving data. It’s about doing it reliably, efficiently, and repeatably, especially when working with cloud data platforms like Azure SQL Data Warehouse (Azure Synapse Analytics). Python is one of the best tools to automate workflows, clean data, and interact with Azure SQL DW seamlessly.
At its core, a data warehouse (DW) is a centralized repository that consolidates data from across the enterprise, spanning sales, finance, marketing, supply chain, and HR into a clean, structured, and analytics-ready format.
Scaling up and scaling down your Azure Dedicated SQL Pool helps optimize both performance and costs.
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