Power BI: Dataflows & Power Query

Introduction to Power Query

Power Query is the data transformation engine used across Power BI, Power Apps, and Power Automate. Based on a functional language called M, it enables importing data from hundreds of sources, applying transformations, combining datasets, and producing consistent models for analysis and visualization.

Originally introduced as an Excel add-in, Power Query became the backbone of data pipelines within the Power Platform ecosystem. Its features include:

• Connecting to a wide range of sources including SQL Server, Azure Synapse, SharePoint, Salesforce, and flat files like CSV or Excel.
• Applying dynamic transformations such as merge, split, pivot, unpivot, filtering, and custom calculations.
• Supporting secure authentication through Azure Active Directory.
• Direct integration with Dataverse and Power BI to ensure data consistency across environments.

Power Query enables a no-code ETL model, making data preparation accessible to business users as well as developers.

What Is a Dataflow in Power BI

Dataflows are cloud implementations of Power Query that allow defining, executing, and scheduling centralized data import and transformation processes. They serve as an intermediate integration layer between data sources and analytical models in Power BI or Dataverse.

As described in Microsoft documentation, Dataflows can be created in the Power Apps Maker Portal or in the Power BI service (app.powerbi.com), providing a web-based design experience powered by Power Query Online.

Main Features of Dataflows

• Import from cloud and on-premises sources using the On-Premises Data Gateway.
• Advanced transformations using Power Query M for data cleansing, normalization, and enrichment.
• Automatic column mapping to existing or dynamically created Dataverse tables.
• Execution as one-time or scheduled runs to ensure periodic data refreshes.
• Sharing Dataflows across multiple reports or Power Platform environments.

A single Dataflow project can manage multiple tables, preserving relationships and referential integrity. However, it is currently limited to a maximum of 500,000 records per execution.

Integration Between Power BI, Dataflows, and Dataverse

A critical aspect of the Power Platform architecture is the native integration between Power BI and Dataverse. Dataflows act as a bidirectional channel to synchronize data between the analytical repository and the application database.

According to best practices from Microsoft Power Platform Enterprise Architecture, Dataflows can be used to:

• Populate Dataverse tables from external sources like SQL, Oracle, or Azure Data Lake.
• Extract data from Dataverse for advanced analytics in Power BI Desktop or the Power BI Service.
• Integrate ETL processes with Azure Data Factory and Synapse Analytics pipelines.

This synergy ensures an up-to-date, consistent, and accessible data model across business layers.

Advanced Configuration and Management

Designing a Dataflow starts with selecting the data source and defining Power Query transformations. You can create dataflows for single imports or scheduled refreshes at defined intervals, configuring time and frequency.

Advanced options include:

• Defining parameters for dynamic connections to variable sources.
• Applying automatic or manual mapping between source columns and Dataverse destinations.
• Integration with Azure Data Factory or Azure Synapse Analytics for complex orchestrations.
• Managing automatic refreshes through the Power BI Service.

For enterprise-grade scenarios, Dataflows can be combined with Power BI Deployment Pipelines to ensure versioning, testing, and promotion across DEV, TEST, and PROD environments.