Dataflows considerations and limitations

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There are a few dataflow limitations across authoring, refreshes, and capacity management that users should keep in mind, as described in the following sections.

General limitations

• Feature parity across government environments can be found in the Power BI feature availability for government article.
• Deleted datasources are not removed from the dataflow datasource page. This is a benign behavior and does not impact the refresh or editing of dataflows. In Lineage View, deleted data sources appear as lineage for a dataflow.
• Deleted datasources will still appear in the Setting page in the gateway drop-down.
• Depth equates to dataflows linked to other dataflows. The current maximum depth is 32.
• Breadth equates to entities within a dataflow.
  • There is no guidance or limits for the optimal number of entities is in a dataflow, however, shared dataflows have a refresh limit of two hours per entity, and three per dataflow. So if you have two entities, and each takes two hours, you shouldn't put them in the same dataflow.
  • For Power BI Premium, guidance and limits are driven by individual use cases rather than specific requirements. The only limit for Power BI Premium is a 24-hour refresh per dataflow.
• A Power BI Premium subscription is required in order to refresh more than ten dataflows cross workspace
• PowerQuery limitations are found in the PowerQuery Online usage limits article.
• Power BI dataflows do not support use of global variables in a URL argument.
• Multi-Geo is currently not supported.
• Vnet support is achieved by using a gateway.
• When using Computed entities with gateway data sources, the data ingestion should be performed in different data sources than the computations. The computed entities should build upon entities that are only used for ingestion, and not ingest data within their own mash-up steps.
• In Power BI dataflows, you can use parameters but you cannot edit them unless you edit the entire dataflow. In this regard parameters in dataflows behave similar to declared constants.

Dataflow authoring

When authoring dataflows, users should be mindful of the following considerations:

• Authoring in Dataflows is done in the Power Query Online (PQO) environment; see the limitations described in Power Query limits. Because dataflows authoring is done in the Power Query Online (PQO) environment, updates performed on the Dataflows workload configurations only impact refreshes, and will not have an impact on the authoring experience

• Dataflows can only be modified by their owners

• Dataflows are not available in My Workspace

• Dataflows using gateway data sources do not support multiple credentials for the same data source

• Using the Web.Page connector requires a gateway

API considerations

More about supported Dataflows REST APIs can be found in the REST API reference. Here are some considerations to keep in mind:

• Exporting and Importing a dataflow gives that dataflow a new ID

• Importing dataflows that contain linked tables will not fix the existing references within the dataflow (these queries should be fixed manually before importing the dataflow)

• Dataflows can be overwritten with the CreateOrOverwrite parameter, if they have initially been created using the import API

Dataflows in shared capacities

There are limitations for Dataflows in shared capacities (non-Premium capacities):

• When refreshing Dataflows, timeouts in a shared capacity are 2 hours per table, and 3 hours per Dataflow
• Linked tables cannot be created in shared Dataflows, although they can exist within the Dataflow as long as the Load Enabled property on the query is disabled
• Computed tables cannot be created in shared Dataflows
• AutoML and Cognitive services are not available in shared Dataflows
• Incremental refresh does not work in shared Dataflows

Dataflows in Premium

Dataflows that exist in Premium have the following considerations and limitations.

Refreshes and data considerations:

• When refreshing Dataflows, timeouts are 24 hours (no distinction for tables and/or dataflows)

• Changing a dataflow from an incremental refresh policy to a normal refresh, or vice versa, will drop all data

• Modifying a dataflow's schema will drop all data

• When using a Premium Per User (PPU) license with dataflows, data is cleared when moving data out of a PPU environment

• When a dataflow is refreshed in a Premium Per User (PPU) context, the data is not visible to non-PPU users

• Incremental refresh works with dataflows only when the enhanced compute engine is enabled

Linked and Computed tables:

• Linked tables can go down to a depth of 32 references

• Cyclic dependencies of linked tables are not allowed

• A linked table can't be joined with a regular table that gets its data from an on-premises data source

• When a query (query A, for example) is used in the calculation of another query (query B) in dataflows, query B becomes a calculated table. Calculated tables cannot refer to on-premises sources.

Compute Engine:

• While using the Compute engine, there is an approximate 10% to 20% initial increase in time for data ingestion.

  • This only applied to the first dataflow that is on the compute engine, and reads data from the data source
  • Subsequent dataflows that use the source dataflow will not incur the same penalty

• Only certain operations make use of the compute engine, and only when used through a linked table or as a computed table. A full list of operations is available in this blog post.

Capacity Management:

• By design, the Premium Power BI Capacities have an internal Resource Manager which throttles the workloads in different ways when the capacity is running on low memory.

  1. For Dataflows, this throttling pressure reduces the number of available M Containers
  2. The memory for Dataflows can be set to 100%, with an appropriately sized container for your data sizes, and the workload will manage the number of containers appropriately

• The approximate number of containers can be found out by dividing the total memory allocated to the workload by the amount of memory allocated to a container

Dataflow usage in datasets

• When creating a dataset in Power BI Desktop, and then publishing it to the Power BI service, ensure the credentials used in Power BI Desktop for the Dataflows data source are the same credentials used when the dataset is published to the service.
  1. Failing to ensure those credentials are the same results in a Key not found error upon dataset refresh

ADLS limitations

• ADLS is not available in GCC, GCC High or DOD environments. See Power BI for US government customers for more information.
• You must be assigned as an owner of the resource, due to changes in the ADLS Gen 2 APIs.
• Azure subscription migration is not supported, but there are two alternatives to do so:
  • First approach: after migration, the user can detach workspaces and reattach them. If using the tenant level account, you must detach all workspaces then detach at the tenant level, and reattach. This can be undesirable for customers who don't want to delete all of their dataflows, or have many workspaces.
  • Second approach: if the previous approach isn't feasible, submit a support request to change the subscription ID in the database.
• ADLS doesn't support most elements in the list in the Directories and file names section of the article for workspace naming and dataflow naming, due to the following limitations:
  • Power BI either returns an unhelpful error, or allows the process to happen but the refresh will fail.
• Cross tenant ADLS subscriptions are not supported. The ADLS attached to Power BI must be part of the same Azure tenant that Power BI uses for Azure Active Directory (Azure AD).

Dataflow data types

The data types supported in dataflows are the following:

Mashup data type	Dataflow data type
Time	Time
Date	Date
DateTime	DateTime
DateTimeZone	DateTimeOffset
Logical	Boolean
Text	String
Any	String
Currency	Decimal
Int8	Int64
Int16	Int64
Int32	Int64
Int64	Int64
Double	Double
Percentage	Double
Single	Double
Decimal	Double
Number	Double
Duration	Not Supported
Binary	Not Supported
Function	Not Supported
Table	Not Supported
List	Not Supported
Record	Not Supported
Type	Not Supported
Action	Not Supported
None	Not Supported
Null	Not Supported

Next steps

The following articles provide more information about dataflows and Power BI:

• Introduction to dataflows and self-service data prep
• Creating a dataflow
• Configure and consume a dataflow
• Configuring Dataflow storage to use Azure Data Lake Gen 2
• Premium features of dataflows
• AI with dataflows
• Dataflows best practices