Power BI Dataflows: When and How to Use Them

Power BI Dataflows enable centralized, reusable data preparation that can be shared across multiple reports and datasets. Instead of duplicating transformation logic in every Power BI Desktop file, dataflows let you define transformations once and reuse them organization-wide. Our Power BI consulting services can help you implement an effective dataflows strategy.

What Are Power BI Dataflows?

Dataflows are cloud-based ETL (Extract, Transform, Load) processes that run in the Power BI service. They use the same Power Query technology you know from Power BI Desktop, but execute in the cloud and store results in Azure Data Lake Storage.

Think of dataflows as reusable data preparation layers. You connect to source systems, apply transformations, and the results become available as a data source for any Power BI report in your organization.

When to Use Dataflows

Multiple Reports Need the Same Data If three different departments create reports from the same CRM data with the same transformations, that logic is duplicated three times. With dataflows, you define the transformation once, and all three reports reference the same prepared data.

Complex Transformations Should Be Centralized When transformations require business logic (calculating customer lifetime value, applying complex pricing rules), centralizing this logic ensures consistency. Changes made to the dataflow automatically propagate to all consuming reports.

Self-Service Users Need Prepared Data Dataflows let IT prepare clean, business-ready data that self-service analysts can use without understanding the source system complexity. Analysts get governed data without the risk of incorrect transformations.

Reducing Load on Source Systems Each Power BI report refreshing against a source database creates load. With dataflows, you query the source once, store the results, and reports query the dataflow instead. This reduces source system impact significantly.

Standardizing Dimension Tables Company-wide dimensions like Date, Product, and Customer should be consistent. Dataflows ensure every report uses the same dimension definitions.

Dataflows Gen1 vs Gen2

Gen1 Dataflows (Standard) - Available in Power BI Pro and Premium - Stores data in Power BI-managed storage - Supports linked and computed entities - Refresh scheduled through Power BI service - Limited to Power Query transformations

Gen2 Dataflows (Fabric) Microsoft Fabric introduces enhanced dataflows with significant improvements:

• Native OneLake storage (Delta Lake format)
• Better performance with Spark-based execution
• Enhanced monitoring and debugging
• Direct Lake mode compatibility
• Integration with other Fabric workloads
• Support for data pipelines orchestration

For new implementations, we recommend Gen2 dataflows when Fabric capacity is available. Learn more about Microsoft Fabric capabilities.

Building Your First Dataflow

Step 1: Create the Dataflow In the Power BI service, navigate to your workspace and select New > Dataflow. Choose whether to start from blank, import an existing model, or link to Azure Data Lake.

Step 2: Connect to Data Sources Use the familiar Power Query interface to connect to your data sources. Dataflows support the same 200+ connectors available in Power BI Desktop including:

• SQL Server, Azure SQL, PostgreSQL
• SharePoint, Excel, CSV files
• REST APIs and OData feeds
• Salesforce, Dynamics 365, SAP

Step 3: Apply Transformations Build your transformation logic using Power Query:

• Filter and sort rows
• Merge and append tables
• Add calculated columns
• Group and aggregate data
• Apply data type conversions

Step 4: Configure Refresh Set up refresh schedules (up to 48 times daily with Premium). Configure incremental refresh for large tables to minimize refresh time and resource usage.

Step 5: Connect Reports to Dataflows In Power BI Desktop, select Get Data > Power Platform > Dataflows. Your dataflow entities appear as data sources that can be loaded or further transformed.

Best Practices for Dataflows

Design for Reusability - Create generic dimensions (Date, Geography) that apply across business areas - Use parameters for values that might change (fiscal year start, currency codes) - Document transformation logic with step names and descriptions

Optimize Performance - Apply filters early in the query to reduce data volume - Use native database folding where possible - Configure incremental refresh for large fact tables - Monitor refresh times and optimize slow queries

Implement Governance - Establish naming conventions (prefix with department or domain) - Control dataflow permissions through workspace security - Certify and endorse trusted dataflows - Maintain documentation for business users

Layer Your Architecture Consider a medallion architecture approach:

• Bronze layer: Raw data ingestion
• Silver layer: Cleaned and conformed data
• Gold layer: Business-ready aggregations

This pattern works especially well with Fabric lakehouses.

Common Dataflow Patterns

Linked Entities Reference entities from other dataflows without duplicating data. A master Customer dataflow can feed multiple department-specific dataflows.

Computed Entities Create entities that compute values from other entities in the same dataflow. Useful for aggregations and summary tables.

Incremental Refresh Configure dataflows to only process new or changed data. Essential for large datasets to maintain reasonable refresh times.

Dataflows vs Other Options

| Feature | Dataflows | Power Query (Desktop) | Azure Data Factory | |---------|-----------|----------------------|-------------------| | Execution | Cloud | Local | Cloud | | Sharing | Organization-wide | Per file | Azure-wide | | Storage | Managed/ADLS | Local | ADLS/SQL | | Complexity | Medium | Low | High | | Cost | Included | Included | Pay-per-use |

Troubleshooting Common Issues

Refresh Failures - Check data source credentials in the Power BI service - Verify gateway configuration for on-premises sources - Review error messages in refresh history - Test queries in Power BI Desktop first

Performance Problems - Implement incremental refresh for large tables - Reduce transformation complexity where possible - Consider breaking large dataflows into smaller ones - Monitor with Power BI monitoring

Data Quality Issues - Add data validation steps in your transformations - Implement error handling for missing or invalid data - Create data quality dashboards to track issues

Ready to implement dataflows in your organization? Contact our team for dataflow architecture guidance.