Adam Marczak explains Azure Data Factory Mapping Data Flow in this video.

With Azure Data Factory Mapping Data Flow, you can create fast and scalable on-demand transformations by using visual user interface. In just minutes you can leverage power of Spark with not a single line of code written.

In this episode I give you introduction to what Mapping Data Flow for Data Factory is and how can it solve your day to day ETL challenges. In a short demo I will consume data from blob storage, transform movie data, aggregate it and save multiple outputs back to blob storage.

Sample code and data: https://github.com/MarczakIO/azure4everyone-samples/tree/master/azure-data-factory-mapping-data-flows 

Here’s an interesting read I discovered via a coworker about the Azure Data Factory Azure Synapse Analytics (Preview).

In Data Factory, an activity defines the action to be performed. A linked service defines a target data store or a compute service.

An integration runtime provides the bridge between the activity and linked Services. It’s referenced by the linked service or activity, and provides the compute environment where the activity either runs on or gets dispatched from.

This way, the activity can be performed in the region closest possible to the target data store or compute service in the most performant way while meeting security and compliance needs.

Integration runtime in Azure Data Factory

Gaurav Malhotra joins Lara Rubbelke to discuss how you can associate a GitHub repository (public & enterprise) to your Azure Data Factory for collaboration, versioning, source control.

 

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