Power Query and Dataflows in Power Platform

Introduction to Dataflows

Dataflows are one of the most powerful components of the Power Platform for importing, transforming, and orchestrating data. Based on Power Query technology, they provide a visual way to connect to multiple data sources, apply transformations, and load results directly into Dataverse.

According to Microsoft Power Platform Enterprise Architecture guidelines, Dataflows are ideal for migration and integration scenarios where data is already consolidated and requires transformation before being loaded into the target system. They allow for both one-time and scheduled imports, embedding lightweight ETL processes directly within the platform.

Power Query: the transformation engine

Power Query is the language and transformation environment powering Dataflows. Originally developed for Excel and Power BI, it is now a native component of Power Platform. It enables data cleaning, modeling, and combination from heterogeneous sources such as SQL Server, Azure Data Lake, Excel, SharePoint, or web APIs.

Through Power Query, you can apply hundreds of transformations: filtering, joins, pivot/unpivot, custom calculations, and handling missing values. The graphical interface automatically generates M code, ensuring process traceability and repeatability.

Dataflows Architecture

A Dataflow is composed of one or more data entities, each representing a logical table or view. Entities are mapped to Dataverse tables or can create new tables “on the fly” during import. This capability allows you to standardize migration processes without manually defining all data structures in advance.

Dataflows can be configured in two main modes:

• Immediate execution: one-time manual import, useful for setup or initial migrations.
• Scheduled execution: periodic synchronization to keep Dataverse data up to date.

As described in architectural manuals, it is possible to import up to 500,000 records per project, providing a good balance between performance and scalability. For on-premises data sources, you must configure the On-Premises Data Gateway to enable secure communication with the cloud.

Typical Dataflow Workflow

The operational process of a Dataflow can be represented in the following diagram:

This flow shows how data is extracted from a source, transformed via Power Query, and then loaded into Dataverse. The entire process can be orchestrated directly from the Power Apps Maker Portal.

Best Practices and Limits

The following recommendations come from best practices outlined in Microsoft Power Platform Enterprise Architecture:

• Design Dataflows for consolidated tables, avoiding fragmentation into too many small flows.
• Use automatic column mapping and verify data type consistency.
• Validate schema before import to prevent consistency errors.
• Integrate post-import verification processes to ensure data correctness.
• Monitor throughput limits and respect the 500,000 records per project threshold.

For more complex scenarios, Microsoft recommends combining Dataflows with tools such as Azure Data Factory or Synapse Link for Dataverse.

Integration with Other Power Platform Tools

Dataflows are not only a migration tool but also a continuous integration channel. They can be shared between Power Apps, Power BI, and Power Automate, enabling a unified view of business data. For example, a Dataflow importing ERP data can simultaneously feed Power BI dashboards and Canvas apps.

Compatibility with Power BI allows Dataflows to be reused directly as data sources for analysis without duplicating transformation pipelines. This convergence reduces maintenance effort and improves data governance.