Dataflows best practices

Power BI dataflows are an enterprise-focused data prep solution, enabling an ecosystem of data that's ready for consumption, reuse, and integration. This article provides a list of best practices, with links to articles and other information that will help you understand and use dataflows to their full potential.

Dataflows across the Power Platform

Dataflows can be used across various Power Platform technologies, such as Power Query, Microsoft Dynamics 365, and other Microsoft offerings. For more information about how dataflows can work across the Power Platform, see using dataflows across Microsoft products.

The following table provides a collection of links to articles that describe best practices when creating or working with dataflows. The links include information about developing business logic, developing complex dataflows, re-use of dataflows, and how to achieve enterprise-scale with your dataflows.

Topic Guidance area Link to article or content
Power Query Tips and tricks to get the most of your data wrangling experience Power Query best practices
Leveraging Computed Tables There are performance benefits for using computed tables in a dataflow Computed Tables Scenarios
Developing complex dataflows Patterns for developing large-scale, performant dataflows Complex dataflows
Reusing dataflows Patterns, guidance, and use cases Reusing dataflows
Large-scale implementations Large-scale use and guidance to complement enterprise architecture Data warehousing using dataflows
Leveraging Enhanced Compute Potentially improve dataflow performance up to 25x Enhanced Compute Engine
Optimizing your workload settings Get the most our of your dataflows infrastructure by understanding the levers you can pull to maximize performance Dataflows workload configuration
Joining and expanding tables Creating performant joins Optimize expanding table operations
Query folding guidance Speeding up transformations using the source system Query folding
Using data profiling Understand column quality, distribution, and profile Data profiling tools
Implementing error handling Developing robust dataflows resilient to refresh errors, with suggestions Patterns for common errors
Complex error handling
Use Schema view Improve the authoring experience when working with a wide table and performing schema level operations Schema view
Linked tables Reusing and referencing transformations Linked Tables
Incremental refresh Load the latest or changed data versus a full reload Incremental refresh

Next steps

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