Fabric’s Lakehouse is a collection of files, folders, tables, and shortcuts that act like a database over a data lake. It’s used by the Spark engine and SQL engine for big data processing and has features for ACID transactions when using the open-source Delta formatted tables.
Fabric’s data warehouse experience allows you to transition from the lake view of the Lakehouse (which supports data engineering and Apache Spark) to the SQL experiences that a traditional data warehouse would provide. The Lakehouse gives you the ability to read tables and use the SQL analytics endpoint, whereas the data warehouse enables you to manipulate the data.
In the data warehouse experience, you’ll model data using tables and views, run T-SQL to query data across the data warehouse and Lakehouse, use T-SQL to perform DML operations on data inside the data warehouse, and serve reporting layers like Power BI.
Now that you understand the basic architectural principles for a relational data warehouse schema, let’s explore how to create a data warehouse.
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