39. Hands-On: ADF Project : Implementing Pivot and Unpivot Transformations & Schedule in Dataflows
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We Will Learn:
How to perform pivot Transformation
How to perform unpivot Transformation
Source Transformation
Pivot Transformation
Unpivot Transformation
Derive column Transformation
Sort Transformations
Sink Transformation
In Azure Data Factory (ADF), triggers are used to execute pipelines automatically based on specific conditions or schedules
Data Factory supports three types of triggers:
Event-based trigger: Triggers pipelines in response to events, such as file creation or deletion in storage services.
Schedule trigger: Executes pipelines at a predefined time and frequency.
Tumbling window trigger: A tumbling window trigger executes pipelines in recurring time intervals (windows), ensuring that each window is processed exactly once, regardless of success or failure. It is useful for processing data in consistent time slices, especially for historical or backdated data or incremental data.
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