Using Dataflows in Power BI: A Comprehensive Guide

Dataflows in Power BI are a powerful way to extract, transform, and load (ETL) data for analysis and reporting. They allow users to create reusable data preparation pipelines that store data in Azure Data Lake and can be used across multiple Power BI datasets.

This guide will take you through every step of creating, managing, and optimizing Power BI Dataflows, from setup to best practices.

1. What Are Dataflows in Power BI?

Dataflows are cloud-based ETL (Extract, Transform, Load) solutions that allow you to ingest, clean, and store data for Power BI reports and dashboards. Unlike regular Power BI datasets, dataflows store data independently, making them reusable across multiple reports.

Key Benefits of Dataflows

✅ Centralized Data Preparation: Create a single source of truth for data.
✅ Reuse Across Reports: Dataflows can be used in multiple Power BI datasets.
✅ Scheduled Data Refresh: Automatically update data at defined intervals.
✅ Handles Large Datasets: Uses Azure Data Lake for efficient storage.
✅ Reduces Load on Source Systems: Process data once and reuse it.

2. Prerequisites for Using Dataflows

Before creating a dataflow, ensure you have:

✔ Power BI Pro or Premium license (Dataflows require at least a Pro license).
✔ Access to Power BI Service (app.powerbi.com).
✔ Data source credentials (e.g., SQL Server, SharePoint, Excel, Web APIs).
✔ Familiarity with Power Query (for data transformation).

3. Creating a Dataflow in Power BI

Step 1: Open Power BI Service

Go to Power BI Service and log in.
Navigate to Workspaces (Dataflows are created inside a workspace).
Select or create a workspace (Make sure it is a Pro or Premium workspace).

Step 2: Create a New Dataflow

Click on New > Dataflow.
Choose “Add new entities” to create a new dataflow.

Step 3: Choose a Data Source

Power BI Dataflows support many data sources, including:

SQL Server
SharePoint
Azure SQL Database
Excel files
OData Feeds
Web APIs
Salesforce, Google Analytics, and more

Select a data source from the list.
Enter the required connection details (e.g., server name, database name).
Provide authentication details (e.g., Windows, Database, OAuth).

Step 4: Extract and Transform Data using Power Query

After connecting to the data source, Power Query Editor will open.
Apply data transformations, such as:
- Removing duplicates
- Filtering rows
- Renaming columns
- Merging tables
- Adding calculated columns
- Changing data types
Click Save & Close once transformations are complete.

Step 5: Define Dataflow Settings

Enter a name for your dataflow.
Choose storage format (Power BI Premium workspaces allow Enhanced Compute Engine).
Click Save.

4. Refreshing a Dataflow

Once the dataflow is created, you need to schedule automatic refreshes to keep data up to date.

Step 1: Set Up Refresh Schedule

Go to Workspace > Dataflows.
Select your dataflow and click on Settings.
Under Scheduled Refresh, click Edit Credentials.
Configure:
- Refresh Frequency: Daily, Weekly, or Custom
- Time Zone: Select the appropriate time zone
- Incremental Refresh (Optional for large datasets)
Click Apply.

Step 2: Manually Refresh (Optional)

If you need to refresh immediately, click Refresh now on the dataflow settings page.

5. Using Dataflows in Power BI Desktop

Once the dataflow is created, you can use it as a data source in Power BI Desktop.

Step 1: Connect to a Dataflow in Power BI Desktop

Open Power BI Desktop.
Click Get Data > Power BI Dataflows.
Select your workspace and choose the desired dataflow table.
Click Load (or Transform Data if further changes are needed).

Step 2: Build Reports and Dashboards

Drag and drop fields into Power BI visuals.
Use DAX calculations for advanced analytics.
Create interactive reports and dashboards.

Step 3: Publish to Power BI Service

Click Publish in Power BI Desktop.
Choose the workspace where the dataflow exists.

6. Optimizing Dataflows for Performance

🔹 Use Incremental Refresh: Only refresh new data instead of the entire dataset.
🔹 Remove Unnecessary Columns: Reduce the data load by selecting only required fields.
🔹 Pre-Aggregate Data: Perform transformations at the dataflow level instead of DAX.
🔹 Use Linked Dataflows: If multiple reports use the same data, create a linked dataflow to improve efficiency.
🔹 Optimize Query Steps: Ensure transformations are applied in the right order (e.g., filtering before merging).

7. Advanced Dataflow Features

7.1 Linked and Computed Dataflows

Linked Dataflows: Reuse existing dataflows across multiple workspaces.
Computed Dataflows: Perform calculations within the dataflow instead of Power BI datasets.

7.2 Incremental Refresh in Dataflows

Available in Power BI Premium.
Allows loading only new data instead of refreshing the entire dataset.
Useful for large data models (e.g., historical data from SQL databases).

8. Common Issues and Troubleshooting

Issue	Solution
Cannot connect to data source	Check credentials and firewall settings
Dataflow refresh fails	Ensure the workspace has Power BI Pro or Premium licensing
Slow dataflow performance	Use incremental refresh and remove unnecessary columns
Data does not update in Power BI Desktop	Refresh the dataset in Power BI Service manually

9. Dataflows vs. Datasets vs. Datamarts

Feature	Dataflows	Datasets	Datamarts
Purpose	Extract, Transform, Load (ETL)	Data modeling and visualization	Low-code database + ETL
Storage	Azure Data Lake	Power BI Service	Azure SQL Database
Reusability	Can be used across reports	Tied to a single report	Can be used across workspaces
Performance	Improves refresh times	Uses memory for processing	Optimized for large datasets

10. Conclusion

Power BI Dataflows are a game-changer for enterprise reporting and data management. They centralize data preparation, improve performance, and enable reuse across multiple reports.

By following this step-by-step guide, you can:
✅ Extract and transform data efficiently using Power Query.
✅ Reuse dataflows across multiple reports for consistency.
✅ Improve report performance with incremental refresh.
✅ Optimize ETL workflows by reducing redundant processing.

Would you like help with DAX calculations for your dataflows?