Use Spark Job Definitions to load data into Fabric warehouse

Hi, Anonymous 

You can use Spark job definitions to perform transformations on your data, including the creation of calculated columns based on existing columns from every table. Spark provides a robust framework for data processing, allowing you to apply complex transformations and calculations directly within your Spark jobs. This approach can indeed help reduce the need for creating calculated columns in your semantic model, potentially simplifying your data model and improving performance.

Lakehouse tutorial - Prepare and transform lakehouse data - Microsoft Fabric | Microsoft Learn

Regarding your question about loading data directly to warehouse tables from Spark jobs, it is possible to load data into warehouse tables, but the approach might vary based on your specific warehouse setup and the capabilities it supports. Typically, you would use Spark to process and prepare your data, and then utilize a connector or API provided by your warehouse to load the data. This process can be automated within your Spark job, allowing for seamless data loading after transformations.

Options to get data into the Lakehouse - Microsoft Fabric | Microsoft Learn

Best Regards,
Yang
Community Support Team

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