It shows the lineage of source data as it flows into one or more sinks. The graph displays the transformation stream. The data flow canvas is separated into three parts: the top bar, the graph, and the configuration panel. Mapping data flow has a unique authoring canvas designed to make building transformation logic easy. For more information, see Source transformation. Select Add source to start configuring your source transformation. This action takes you to the data flow canvas, where you can create your transformation logic. To create a data flow, select the plus sign next to Factory Resources, and then select Data Flow. Getting startedÄata flows are created from the factory resources pane like pipelines and datasets.
Azure Data Factory handles all the code translation, path optimization, and execution of your data flow jobs. Your data flows run on ADF-managed execution clusters for scaled-out data processing. Mapping data flows provide an entirely visual experience with no coding required. Data flow activities can be operationalized using existing Azure Data Factory scheduling, control, flow, and monitoring capabilities. The resulting data flows are executed as activities within Azure Data Factory pipelines that use scaled-out Apache Spark clusters.
Data flows allow data engineers to develop data transformation logic without writing code. Mapping data flows are visually designed data transformations in Azure Data Factory. Azure Synapse Analytics What are mapping data flows?