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 What are dataflows.
Deleted dataflows can't be recovered
Deleted dataflows can't be recovered, but you can back them up with various methods described in this section.
If you enable the Azure storage connection on your Power BI workspace, a copy of your dataflow definition and snapshots are automatically stored in a data lake. You can then recover a deleted or modified dataflow by downloading its model.json file from the data lake, then importing it back to Power BI.
You can use Power Automate or Azure Logic Apps to export your dataflow definition to a JSON file, then store it in SharePoint or Azure Data Lake Gen2. Using either of these methods enables you to back up your dataflow using alternative file storage options and automate the process.
You can also manually export your dataflow to a JSON file and import it to another workspace or location. Manually exporting your dataflow is simple and quick, but is a manual process that must be done each time you want to back up your dataflow.
Dataflows best practices table and links
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, reuse 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 | Best practices when working with Power Query |
| Using computed tables | Performance benefits for using computed tables in a dataflow | Computed tables scenarios |
| Developing complex dataflows | Patterns for developing large-scale, performant dataflows | Best practices for designing and developing complex dataflows |
| Reusing dataflows | Patterns, guidance, and use cases | Best practices for reusing dataflows across environments and workspaces |
| Large-scale implementations | Large-scale use and guidance to complement enterprise architecture | Best practices for creating a dimensional model using dataflows |
| Using enhanced compute | Potentially improve dataflow performance up to 25x | Using the compute engine to improve performance |
| Optimizing your workload settings | Get the most out of your dataflows infrastructure by understanding the levers you can pull to maximize performance | Configure Power BI Premium dataflow workloads |
| Joining and expanding tables | Creating performant joins | Optimize Power Query when expanding table columns |
| Query folding guidance | Speeding up transformations using the source system | Power Query query folding |
| Using data profiling | Understand column quality, distribution, and profile | Using the data profiling tools |
| Implementing error handling | Developing robust dataflows resilient to refresh errors, with suggestions | Dealing with errors in Power Query 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 | Create a dataflow by using linked tables |
| Incremental refresh | Load the latest or changed data versus a full reload | Using incremental refresh with dataflows |
Related content
The following articles provide more information about dataflows and Power BI: