Fabric Data Factory

You can build data pipelines in Fabric Data Factory by orchestrating Copy, Dataflow Gen2, Notebook, and Script activities. This enables scalable data integration, batch ETL, and cross-workspace data movement across lakehouse and warehouse workloads.

Dataflow Gen2 is used for data transformation within Fabric Data Factory pipelines. It allows you to stage data to a lakehouse and perform reusable transformations alongside Copy, Notebook, and Script activities for end-to-end orchestration.

Yes, you can schedule triggers for Fabric Data Factory pipelines to automate data movement workflows. This supports functional requirements for monitoring and executing batch ETL and incremental loads across Fabric data services.

Fabric Data Factory supports cross-workspace data movement through reusable pipelines. It uses broad connector management to move and transform data across lakehouse, warehouse, and notebook workloads for end-to-end data integration.

The best way to orchestrate notebooks and dataflows together is using Fabric Data Factory pipelines. You can chain Notebook and Dataflow Gen2 activities within a single pipeline to execute scalable data transformations and staging.

You need Fabric Data Factory for ETL when requiring robust scheduling, triggers, and broad connector management across lakehouse and warehouse workloads. It orchestrates multiple notebook and dataflow activities end-to-end, which standalone notebooks lack.