With Azure Data Factory Self-hosted integration runtime, you can now integrate your on-premise, virtual private network data sources as well as those which require your own drivers.
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.
Choosing the right trigger type is very important task when designing data factory workflows. Today I will show you four ways to trigger data factory pipelines so you can make sure you react to your business needs better.
Parametrization in Azure Data Factory is essential to achieve good design and reusability as well as low cost of solution maintenance. Using parameters also speeds up implementation of new features in your pipelines.
Azure Data Factory is essential service in all data related activities in Azure. It is flexible and powerful Platform as a Service offering with multitude of connectors and inetgration capabilities. It is a heart of ETL in Azure.