Understanding Microsoft Fabric: Creating a Data Warehouse

Introduction to Microsoft Fabric

Microsoft Fabric is essentially an end-to-end analytics product in the Power BI service, integrating various technologies such as Azure Synapse Analytics, Azure Data Factory, and Data Bricks notebooks into a unified Power BI interface.

Differences Between Lakehouse and Warehouse

One of the main differences between a Lakehouse and a Warehouse in Fabric is the type of data they handle. A Lakehouse can manage structured, unstructured, and semi-structured data, while a Warehouse is specifically designed for structured data only.

Moreover, a Lakehouse allows for read operations in SQL, whereas a Warehouse enables both read and write operations. This flexibility is crucial, and today, I want to show you how to create a Warehouse and transfer data from a Lakehouse into it, giving you a more traditional SQL Server experience.

Creating a Data Warehouse

Inside my Power BI workspace, you will notice various objects, including the Adventure Works Lakehouse. By clicking on it, you can explore the different tables available. All tables in the Lakehouse are essentially files backed by the OneLake data lake, allowing for seamless data integration from various sources.

To create a new Warehouse, navigate to the drop-down menu in the workspace. If you don’t see the Warehouse option, you might need to switch your persona within the Fabric-enabled workspace. Here, you can select the Data Engineering or Data Science workspace as needed.

After selecting the Warehouse option, you can create a new Warehouse by inputting a name. For instance, I will create an Adventure Works Data Warehouse named “awDW”. This will open up various options for you to perform SQL operations that are typically unavailable in a Lakehouse.

Using SQL Operations in Your Warehouse

Now that we have our Warehouse set up, we can create tables using T-SQL. I will create a new SQL query to select data from the Adventure Works Lakehouse into the Warehouse. This query will allow me to select all data from the “dim currency” table in the Lakehouse and insert it into a new table in the Warehouse.

Here’s a simple SQL statement to achieve this:

SELECT * INTO awDW.dbo.dim_currency FROM adventureworksLakehouse.dbo.dim_currency;

Running this query will create a new table named “dim_currency” in the Warehouse, populated with data from the Lakehouse. After executing the query, refresh the Warehouse to see your new table listed under the dbo schema.

Conclusion

This process illustrates how the Data Warehouse in Microsoft Fabric allows for traditional SQL operations, making it a powerful tool for those familiar with SQL Server. Whether you’re transitioning from an on-premises SQL Server background or coming from a Lakehouse environment, Microsoft Fabric provides a collaborative workspace for all your data needs.

For more content on Microsoft Fabric and related technologies, be sure to subscribe to our channel. If you’re eager to deepen your knowledge, check out our On-Demand Learning platform for in-depth classes on various technologies at your own pace.

Stay tuned for more exciting updates as Microsoft Fabric continues to evolve and improve!