Dataflows Demystified: Streamline Your Power BI Data Prep
Data is like fuel for analytics, and it's increasing very fast. As the amount of data grows, it becomes harder to organize it into useful and clear information. We need data that's clean and ready to use in visuals, reports, and dashboards so we can quickly turn it into helpful insights.
Power BI Dataflow:
A Dataflow is a group of tables that you create and manage in Power BI workspaces. A table is just a set of columns used to store data, similar to a table in a database. You can also think of Dataflow as Power Query Online, where you can run all Power Query steps directly in the cloud without needing Power BI Desktop.
Why do we need to use it?
- Set up data cleaning and shaping steps once, and use them across multiple reports and data models in Power BI—saving time and keeping your logic consistent.
- Save your transformed data in your own Azure Data Lake Gen 2. This lets you use the data not just in Power BI, but also in other Azure services.
- Turn raw data into clean, reliable information using standard rules. This trusted data can then be used in Power Platform tools like Power Apps and Power Automate.
- Keep your original data sources safe by only sharing the cleaned and approved version with report builders through Power BI dataflows.
General Limitations
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Maximum linked dataflow depth: 32 levels.
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Refresh limits (shared capacity): 2 hours per table, 3 hours per dataflow.
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Refresh limits (Premium): Up to 24 hours per dataflow.
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Dataflows not editable in My Workspace.
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Power BI Premium needed to refresh more than 10 dataflows across workspaces.
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Multi-Geo not supported unless using your own ADLS Gen2.
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VNet support requires gateway.
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Parameters behave like constants and can’t be edited without editing the entire dataflow.
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Case-sensitive slicer search in DirectQuery on dataflows.
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Incremental refresh not supported in shared capacity; works in Premium with enhanced compute engine.
Dataflow Authoring
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Done in Power Query Online (PQO).
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Only owners can modify dataflows.
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Multiple credentials not supported for the same gateway data source.
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Removing an on-premises Data Gateway connection may need manual steps or recreating the dataflow.
API Considerations
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Importing a dataflow gives it a new ID.
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Linked tables need manual updates after import.
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CreateOrOverwrite parameter allows replacement via API.
Premium vs Shared Capacity
Shared Capacity:
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No linked or computed tables.
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No AutoML or Cognitive Services.
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No incremental refresh.
Premium Capacity:
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Full feature access.
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Linked table depth: 32; no cyclic dependencies.
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Schema or refresh policy changes clear all data.
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PPU limitations: data not visible to non-PPU users.
Compute Engine
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First use has a 10–20% ingestion time penalty.
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Works only for specific transformations.
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Boosts performance in linked or computed entities.
Semantic Models
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Credential mismatch between Desktop and Service causes refresh failures.
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Semantic model must be updated and re-published if the dataflow structure changes.
Named Connections & ADLS Limitations
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Only one cloud connection per type/path allowed.
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ADLS not available in GCC, DOD, or GCC High environments.
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ADLS and Power BI must be in the same Azure tenant.
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ADLS subscription migration is not directly supported—workaround requires detaching workspaces or submitting a support request.
- Break large dataflows into smaller, reusable components.
- Use linked entities to reference data across dataflows.
- Use meaningful, consistent names for dataflows, queries, and fields.
- This improves readability and collaboration.
- Create a “golden dataflow” with standardized business rules.
- Let reports pull data from this central dataflow to maintain consistency.
- Pre-calculate heavy transformations once and reuse them downstream.
- Use Enhanced Compute Engine (Premium Only)
- Enable the compute engine to improve performance up to 25x.
- Implement Incremental Refresh
- Refresh only new or changed records instead of full datasets.
- Avoid Unnecessary Columns and Rows
- Filter early to reduce data volume for better speed and efficiency.
- Let Power Query push transformations to the data source when possible.
- This speeds up refresh and reduces load.
- Use Data Profiling tools to check column quality, distribution, and completeness.
- Use `try...otherwise` patterns in M code to handle exceptions and prevent refresh failures.
- Store dataflow definition and snapshots for versioning and backup.
- Use Power Automate or Azure Logic Apps to automate dataflow JSON export.
- Backup manually when needed.
- Create a single source of truth and link it across reports or environments.
- Make your queries parameterized for reuse in multiple contexts.
- Control access to dataflows to limit sensitive data exposure.
- Maintain internal documentation for each dataflow’s purpose, structure, and refresh schedule.
- Avoid overloading capacity by scheduling refreshes at different intervals.
- Use the Refresh history and Power BI activity logs to track and optimize.
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