Optimizing Data Refresh with Incremental Load in Power BI

Incremental Load is a Power BI feature that allows you to update and refresh a dataset without reloading the entire dataset. In Power BI, implementing incremental load can significantly reduce the time by refreshing and archiving a subset of data.

Benefits of Incremental Load

1. Efficiency: Reduces the time taken to refresh data by only loading new or changed data.
2. Performance: Minimizes the impact on data sources and network load.
3. Scalability: Handles growing data volumes more effectively.
4. Resource Optimization: Lowers the computational and memory usage during data refresh.

Requirements for Incremental Load in Power BI

To implement incremental load in Power BI, you need:

• Power BI Pro or Premium: Incremental refresh is a feature available in Power BI Pro and Premium licenses.
• Data Source Requirements:-
• The data source must support query folding.
• The data source must have a column that can be used to identify new or changed records, typically a date/time or integer column.

Steps to Configure Incremental Load

Prepare the Data

Identify a column in your dataset that can be used for incremental load, such as a date/time or integer column that indicates when the data was last updated or inserted.

1. First, let’s open Power BI Desktop and go to the Home tab.

2. Click on the Transform Data option from the ribbon, to open the Power Query Editor.

3. Go to the Manage Parameter from Home tab ribbon, then click New Parameter

4. Create Parameters:

• RangeStart: Set the data type to Date/Time and provide a default start date value.
• RangeEnd: Set the data type to Date/Time and provide a default end date value.

5. Filter the Data:

• Use the parameters to filter the data in your query.
• In Power Query Editor, select the table you want to load incrementally.
• Apply a filter on the date column using the RangeStart and RangeEnd parameters.

Configure Incremental Refresh

• Close and apply the changes in Power Query Editor.
• In Power BI Desktop, select the table for which you want to configure incremental refresh
• Go to the Data tab and click on Incremental Refresh

7. Configure the incremental refresh policy:

• Incremental refresh range: Specify how long historical data should be stored (e.g., last 2 years).
• Incremental refresh partition: Specify the period for incremental refresh (e.g., last 1 day).

8. Publish and Test

1. Save and publish the Power BI report to the Power BI Service.

2. Configure a scheduled refresh in the Power BI Service to automate the data load process.

3. For the incremental refresh testing, we have uploaded two reports in the Power BI service within the workspace. One is for Incremental refresh and one is without incremental refresh.

We have logged the duration for the dataset refresh.

Without incremental data model duration:- 02 minutes and 41 seconds

Incremental data model duration:- 45 seconds

Summary

Imagine you have a sales database with millions of rows of transaction data. You want to update your Power BI report daily with only the new sales data from the previous day. Here’s how you can achieve this with incremental load:

• Identify the Date Column: Use the TransactionDate column in your sales table.
• Create Parameters: RangeStart and RangeEnd with default values set to the current date and time.
• Filter Data: Apply a filter in Power Query to load data where TransactionDate is between RangeStart and RangeEnd.
• Configure Incremental Refresh: Set only the last 5 years of historical data and refresh the last 1 day incrementally.
• Publish: Publish the report and set up a daily scheduled refresh.

Important

• Data Source Considerations: Ensure your data source supports query folding for efficient incremental refresh.
• Testing: Test the incremental load setup in a development environment before deploying to production.
• Monitor Refresh: Regularly monitor the data refresh process to ensure it is working as expected and troubleshoot any issues.
• Performance Tuning: Optimize queries and consider partitioning large tables to enhance performance.

Conclusion

Implementing incremental load in Power BI is a powerful way to optimize data refresh processes for large datasets. By only loading new or changed data, you can improve efficiency, reduce load times, and make better use of resources. Follow the steps outlined above to configure incremental load and take advantage of this feature in your Power BI projects.