What are data flows in Azure Synapse Analytics?

Data flows are visually designed data transformations in Azure Synapse Analytics. Data flows allow data engineers to develop data transformation logic without writing code. The resulting data flows are executed as activities within Azure Synapse Analytics pipelines that use scaled-out Apache Spark clusters. Data flow activities can be operationalized using existing Azure Synapse Analytics scheduling, control, flow, and monitoring capabilities.

Data flows provide an entirely visual experience with no coding required. Your data flows run on Synapse-managed execution clusters for scaled-out data processing. Azure Synapse Analytics handles all the code translation, path optimization, and execution of your data flow jobs.

Get started

Data flows are created from the Develop pane in Synapse studio. To create a data flow, select the plus sign next to Develop, and then select Data Flow.

This action takes you to the data flow canvas, where you can create your transformation logic. Select Add source to start configuring your source transformation. For more information, see Source transformation.

Authoring data flows

Data flow has a unique authoring canvas designed to make building transformation logic easy. The data flow canvas is separated into three parts: the top bar, the graph, and the configuration panel.

Graph

The graph displays the transformation stream. It shows the lineage of source data as it flows into one or more sinks. To add a new source, select Add source. To add a new transformation, select the plus sign on the lower right of an existing transformation. Learn more on how to manage the data flow graph.

Configuration panel

The configuration panel shows the settings specific to the currently selected transformation. If no transformation is selected, it shows the data flow. In the overall data flow configuration, you can add parameters via the Parameters tab. For more information, see Data flow parameters.

Each transformation contains at least four configuration tabs.

Transformation settings

The first tab in each transformation's configuration pane contains the settings specific to that transformation. For more information, see that transformation's documentation page.

Optimize

The Optimize tab contains settings to configure partitioning schemes. To learn more about how to optimize your data flows, see the mapping data flow performance guide.

Inspect

The Inspect tab provides a view into the metadata of the data stream that you're transforming. You can see column counts, the columns changed, the columns added, data types, the column order, and column references. Inspect is a read-only view of your metadata. You don't need to have debug mode enabled to see metadata in the Inspect pane.

As you change the shape of your data through transformations, you see the metadata changes flow in the Inspect pane. If there isn't a defined schema in your source transformation, then metadata isn't visible in the Inspect pane. Lack of metadata is common in schema drift scenarios.

Data preview

If debug mode is on, the Data Preview tab gives you an interactive snapshot of the data at each transform. For more information, see Data preview in debug mode.

Top bar

The top bar contains actions that affect the whole data flow, like validation and debug settings. You can view the underlying JSON code and data flow script of your transformation logic as well.

Available transformations

View the mapping data flow transformation overview to get a list of available transformations.

Data flow activity

Data flows are operationalized within Azure Synapse Analytics pipelines using the data flow activity. All a user has to do is specify which integration runtime to use and pass in parameter values. For more information, learn about the Azure integration runtime.

Debug mode

Debug mode allows you to interactively see the results of each transformation step while you build and debug your data flows. The debug session can be used both in when building your data flow logic and running pipeline debug runs with data flow activities. To learn more, see the debug mode documentation.

Monitoring data flows

Data flow integrates with existing Azure Synapse Analytics monitoring capabilities. To learn how to understand data flow monitoring output, see monitoring mapping data flows.

The Azure Synapse Analytics team has created a performance tuning guide to help you optimize the execution time of your data flows after building your business logic.

Related content

• Learn how to create a source transformation.
• Learn how to build your data flows in debug mode.