Implement incremental refresh (including the use of query folding)

Implementing incremental refresh in Microsoft Power BI allows you to efficiently refresh large datasets by updating only the modified or new data instead of refreshing the entire dataset. This can significantly reduce the time and resources required for data refreshes. In addition, query folding can be applied to optimize query performance by pushing data transformation and filtering operations to the data source.

What is incremental refresh?

Incremental refresh is a feature in Power BI that enables you to refresh portions of your dataset based on specified criteria. Rather than refreshing the complete dataset, only the data that has changed or is new since the last refresh is updated. This is especially useful for large datasets where refreshing the entire dataset may be time-consuming and resource-intensive.

By implementing incremental refresh, you can break down your dataset into smaller partitions based on a defined column or query range. You can then configure the refresh policy for each partition to determine how data updates are handled. This ensures that only the necessary data is refreshed, resulting in faster and more efficient refresh times.

Benefits of incremental refresh

Implementing incremental refresh provides several benefits:

• Improved refresh performance: By refreshing only the modified or new data, you can significantly reduce the time required for refreshes, especially for large datasets.
• Reduced resource consumption: Incremental refresh minimizes the resources required for refreshing datasets by avoiding the need to process and load redundant data.
• Efficient storage utilization: With incremental refresh, you can store only the necessary partitions, reducing the amount of storage space required for your datasets.
• Scalability: Incremental refresh allows you to partition your datasets and refresh them separately, making it easier to manage and scale as your data grows.

Implementing incremental refresh

To implement incremental refresh in Power BI, follow these steps:

1. Open Power BI Desktop and open the dataset you want to enable incremental refresh for.
2. Go to the “Modeling” tab and click on “Incremental Refresh” under the “Table Tools” section.
3. In the Incremental Refresh dialog, choose the table or tables you want to apply incremental refresh to.
4. Configure the partition column or query range that will be used to divide the dataset into logical segments. This column should have a consistent range of values, such as dates or sequential numbers.
5. Specify the refresh policy for each partition. You can choose between two options:
   • “Refresh rows in the query range”: Only the rows that fall within the defined query range will be refreshed.
   • “Refresh rows in the query range and detect updates in the source”: In addition to refreshing the rows in the query range, Power BI will also compare the actual data in the data source to identify any modified or new rows.
6. Set the refresh policy and configure other options such as partitioning options and retention policy according to your requirements.
7. Click on “OK” to save the incremental refresh settings.

Remember to publish the dataset to Power BI service for incremental refresh to take effect. You can schedule the dataset refresh in Power BI service to ensure regular updates.

Query folding and incremental refresh

Query folding is a technique used in Power Query to push data transformation and filtering operations to the data source, allowing you to leverage the performance optimizations provided by the underlying data source. When query folding is applied during incremental refresh, Power BI sends optimized queries to the data source to fetch only the necessary data, resulting in faster and more efficient refreshes.

To ensure query folding is utilized during incremental refresh, you need to follow these best practices:

1. Use supported data sources: Query folding is supported for a wide range of data sources, including SQL Server, Oracle, SharePoint, and more. Ensure that your data source supports query folding for optimal performance.
2. Apply transformations before partitioning: Apply necessary data transformations, filters, and aggregations before implementing incremental refresh. This ensures that these operations are applied to the data source before the data is divided into partitions, enabling query folding.
3. Avoid unsupported operations: Some operations, such as custom functions or unsupported query folding operations, may prevent query folding from occurring. Avoid using these operations when implementing incremental refresh to maximize query folding optimizations.

By applying these best practices, you can optimize query performance during incremental refresh by leveraging query folding to push operations to the data source.

Conclusion

Implementing incremental refresh along with query folding in Power BI allows you to efficiently refresh large datasets by updating only the modified or new data. This reduces refresh times, optimizes query performance, and improves overall efficiency in handling large volumes of data.

By following the steps outlined in this article and leveraging the best practices for query folding, you can maximize the benefits of incremental refresh and ensure optimal performance for your enterprise-scale analytics solutions using Microsoft Azure and Power BI.