Load data into a lakehouse

Fabric lakehouses are a central element for your analytics solution. You can follow the ETL (Extract, Transform, Load) process to ingest and transform data before loading to the lakehouse.

You can ingest data in many common formats from various sources, including local files, databases, or APIs. You can also create Fabric shortcuts to data in external sources, such as Azure Data Lake Store Gen2 or OneLake. Use the Lakehouse explorer to browse files, folders, shortcuts, and tables and view their contents within the Fabric platform.

Ingested data can be transformed and then loaded using either Apache Spark with notebooks or Dataflows Gen2. Use Data Factory pipelines to orchestrate your different ETL activities and land the prepared data into your lakehouse.

You can use your lakehouse for many reasons, including:

  • Analyze using SQL.
  • Train machine learning models.
  • Perform analytics on real-time data.
  • Develop reports in Power BI.

Secure a lakehouse

Lakehouse access is managed either through the workspace or item-level sharing. Workspaces roles should be used for collaborators because these roles grant access to all items within the workspace. Item-level sharing is best used for granting access for read-only needs, such as analytics or Power BI report development.

Fabric lakehouses also support data governance features including sensitivity labels, and can be extended by using Microsoft Purview with your Fabric tenant.



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