What are mapping data flows?

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

Mapping data flows provide a fully visual experience with no coding required. Your data flows will run on your own execution cluster for scaled-out data processing. Azure Data Factory handles all the code translation, path optimization, and execution of your data flow jobs.

Getting started

To create a data flow, select the plus sign under Factory Resources, and then select Data Flow.

New data flow

This 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.

Data flow canvas

The data flow canvas is separated into three parts: the top bar, the graph, and the configuration panel.



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.


Azure integration runtime data flow properties

Debug button

When you begin working with data flows in ADF, you will want to turn on the "Debug" switch for data flows at the top of the browser UI. This will spin-up an Azure Databricks cluster to use for interactive debugging, data previews, and pipeline debug executions. You can set the size of the cluster being utilized by choosing a custom Azure Integration Runtime. The debug session will stay alive for up to 60 minutes after your last data preview or last debug pipeline execution.

When you operationalize your pipelines with data flow activities, ADF will use the Azure Integration Runtime associated with the activity in the "Run On" property.

The default Azure Integration Runtime is a small 4-core single worker node cluster intended to allow you to preview data and quickly execute debug pipelines at minimal costs. Set a larger Azure IR configuration if you are performing operations against large datasets.

You can instruct ADF to maintain a pool of cluster resources (VMs) by setting a TTL in the Azure IR data flow properties. This will result in faster job execution on subsequent activities.

Azure integration runtime and data flow strategies

Execute data flows in parallel

If you execute data flows in a pipeline in parallel, ADF will spin-up separate Azure Databricks clusters for each activity execution based on the settings in your Azure Integration Runtime attached to each activity. To design parallel executions in ADF pipelines, add your data flow activities without precedence constraints in the UI.

Of these three options, this option will likely execute in the shortest amount of time. However, each parallel data flow will execute at the same time on separate clusters, so the ordering of events is non-deterministic.

If you are executing your data flow activities in parallel inside your pipelines, it is recommended to not use TTL. This is because parallel executions of data flows simultaneously using the same Azure Integration Runtime will result in multiple warm pool instances for your data factory.

Overload single data flow

If you put all of your logic inside a single data flow, ADF will all execute in that same job execution context on a single Spark cluster instance.

This option can possibly be more difficult to follow and troubleshoot because your business rules and business logic will be jumble together. This option also doesn't provide much re-usability.

Execute data flows serially

If you execute your data flow activities in serial in the pipeline and you have set a TTL on the Azure IR configuration, then ADF will reuse the compute resources (VMs) resulting in faster subsequent execution times. You will still receive a new Spark context for each execution.

Of these three options, this will likely take the longest time to execute end-to-end. But it does provide a clean separation of logical operations in each data flow step.

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 edit the name and description under the General tab or add parameters via the Parameters tab. For more information, see Mapping data flow parameters.

Each transformation has 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.

Source settings tab


The Optimize tab contains settings to configure partitioning schemes.


The default setting is Use current partitioning, which instructs Azure Data Factory to use the partitioning scheme native to data flows running on Spark. In most scenarios, we recommend this setting.

There are instances where you might want to adjust the partitioning. For instance, if you want to output your transformations to a single file in the lake, select Single partition in a sink transformation.

Another case where you might want to control the partitioning schemes is optimizing performance. Adjusting the partitioning provides control over the distribution of your data across compute nodes and data locality optimizations that can have both positive and negative effects on your overall data flow performance. For more information, see the Data flow performance guide.

To change the partitioning on any transformation, select the Optimize tab and select the Set Partitioning radio button. You'll then be presented with a series of options for partitioning. The best method of partitioning will differ based on your data volumes, candidate keys, null values, and cardinality.

A best practice is to start with default partitioning and then try different partitioning options. You can test by using pipeline debug runs, and view execution time and partition usage in each transformation grouping from the monitoring view. For more information, see Monitoring data flows.

The following partitioning options are available.

Round robin

Round robin is a simple partition that automatically distributes data equally across partitions. Use round robin when you don't have good key candidates to implement a solid, smart partitioning strategy. You can set the number of physical partitions.


Azure Data Factory will produce a hash of columns to produce uniform partitions such that rows with similar values will fall in the same partition. When you use the Hash option, test for possible partition skew. You can set the number of physical partitions.

Dynamic range

Dynamic range will use Spark dynamic ranges based on the columns or expressions that you provide. You can set the number of physical partitions.

Fixed range

Build an expression that provides a fixed range for values within your partitioned data columns. To avoid partition skew, you should have a good understanding of your data before you use this option. The values you enter for the expression will be used as part of a partition function. You can set the number of physical partitions.


If you have a good understanding of the cardinality of your data, key partitioning might be a good strategy. Key partitioning will create partitions for each unique value in your column. You can't set the number of partitions because the number will be based on unique values in the data.


The Inspect tab provides a view into the metadata of the data stream that you're transforming. You can see the column counts, columns changed, columns added, data types, column ordering, 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'll see the metadata changes flow in the Inspect pane. If there isn't a defined schema in your source transformation, then metadata won't be 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 saving and validation. You can also toggle between graph and configuration modes by using the Show Graph and Hide Graph buttons.

Hide graph

If you hide your graph, you can browse through your transformation nodes laterally via the Previous and Next buttons.

Previous and next buttons

Next steps