How to Setup Incremental Refresh in Power BI [Step by Step Guide]

Microsoft’s Power BI is a tool developed by Microsoft for business analytics to visualize and share insights from their data. Organizations are collecting more and more data, so the need to manage large datasets in an effective way is becoming critical. Incremental refresh is one of the features to solve this problem. Power BI incremental refresh lets you load only the new data or modified rows into an already published dataset instead of replacing all the existing records with a full schedule. This not only saves time but also streamlines the use of resources. This is an instructional guide, so by following the upcoming lines, you can perform incremental loads in your Power BI to keep data up-to-date without hammering resources.

Why Use Incremental Refresh in Power BI?

The point of incremental refresh in Power BI is that it delivers the following advantages for managing data within Power BI:

• Efficiency – As it only updates the new or changed data, refreshes are much faster than over a full dataset with the ALL() function. It is especially powerful in speeding up refreshes for large datasets that would otherwise be impacted by full loads. This CPU savings translates into lower costs and stability.
• Better Resource Utilization: Since only a limited amount of data is processed, incremental refresh saves the additional overhead on both your data source and Power BI service. By following this practice, you can stop affecting your performance and help streamline how Power BI works under the hood.
• Scale: As your data grows larger and larger, it becomes even more important to manage large datasets efficiently. Incremental Refresh is designed to scale and get you showing more rows while still keeping performance.
• Where Less Resources Consumed: Lower resource consumption means that your application can enjoy better stable operation and lower operational costs. Achieving this goal can be done by reducing the amount of data being processed in each renewal, which will give your computer systems greater capacity for cost management and control over computing resources.

Requirements for Incremental Refresh

Before starting with the setup, certain prerequisites must be met to implement incremental refresh in Power BI:

• Pro or Premium License of Power BI: Incremental refresh can be found for Pro and premium users. License the account you use to configure this feature properly.
• Download and Install the Latest Power BI Desktop: Continued improvements and features mean you have the most up-to-date application possible.
• Data Source: Should be from a query folding supporting data source. Folding is the term used when Power Query in incremental refresh can push transformations back to the data source for processing. Query folding is supported by common data sources such as SQL Server, Azure SQL Database, Oracle, SAP HANA, and Teradata.
• Date Column: In the dataset, you will need a time column because this column will be used to partition the Incremental Refresh datasets. This will allow for faster searching and easier cursor sessions by end users more familiar with “time” practices than data tables.

How to Set Up Incremental Refresh

This can be a long and painful process where you could easily make mistakes along the way. Let’s take this step by step:

1. Power BI Incremental Refresh Parameter

As seen above, Power BI incremental refresh parameters are very important to specify the date range of data to refresh inside a table. Here’s how to set them up:

Step 1: Launch Power BI Desktop Open up the application and load your data.

Create Parameters:

Navigate to the “Home” tab, click on “Manage Parameters,” and choose ‘New Parameter…”

As always, you will need to enter two parameters: RangeStart and RangeEnd.

RangeStart: Set the data type to Date/Time and a default value.

RangeEnd: Date and Time data type, with a default value.

These are the parameters that will determine the beginning and end of a data range to do incremental refresh.

2. How to Set Up Incremental Refresh in Power BI Desktop

After you define the parameters, now we only have to tell our dataset to use these for incremental refresh.

Filter Data Using Parameters:

Select “Transform Data” to open the query editor.

Select the date column and enter a filter using RangeStart and RangeEnd.

Filter Example: [DateColumn] >= RangeStart and [DateColumn] < RangeEnd

This step verifies that the dataset has just information in a given range.

Definition of Incremental Refresh Policy

After that, you can close the query editor and go back to the main Power BI Desktop screen.

Right-click on the table in the fields pane Table Options Incremental refresh.

Configure the Settings:

Archive data for: Specify the number of years to retain (i.e., 5 years).

Incrementally refresh data on: Incremental refresh period (example, last 1 month).

If needed, enable options like Detect data changes.

These settings determine the data retention and refresh policy.

Publish to Power BI Service:

Save and publish your report to the Power BI Service. This will enable your dataset to be available for scheduled refresh in the Power BI cloud.

3. Enabling Incremental Refresh in Power BI Service

Once your report is published, you will need to configure the refresh schedule in the Power BI service:

Schedule Refresh:

Go to the dataset settings in Power BI Service.

Set the data source credentials so Power BI can get to your information again for scheduled refreshes.

Set the Refresh Frequency (Daily, Weekly). The frequency depends on how frequently your data updates and how current you need the reports.

Monitor Refreshes:

Review the Refresh History for signs that your incremental refresh is functioning properly. By monitoring, you can identify issues that occur throughout a refresh and diagnose them.

Full Refresh vs. Incremental Refresh

Full Refresh

When a refresh is full in Power BI, it reloads the complete dataset again from the data source wherever you initiate your refresh. While it is simple, processing every record afresh each time you pull a dataset can be slow and expensive for very large datasets.

Full refresh fully reloads the entire dataset from the data source.

Full refresh is best used with small datasets or where the entire dataset needs to be reprocessed in case of any changes. It also comes in handy where you have to make sure that all data (including historical records) is accurate to date.

Full refreshes, as the name suggests, require all of your data to be uniquely acquired and processed periodically, which can quickly become very resource-hungry and sometimes time-consuming for large datasets. Every refresh cycle will force the system to restore and calculate the full dataset, which might introduce quite some delay as well as more pressure on both the data source and Power BI service.

For Example: Consider that you have a dataset for an organization involved in retail business standing sales records from many past years. If this is a full refresh dataset, sales records from the inception of your business through today will need to be loaded and processed every time you reload that dataset.

Incremental Refresh

On the other hand, incremental refresh will only update or exchange data from some time before. This is implemented to improve performance, managing the big datasets and only tracking changes.

Incremental refresh will only refresh the data that is updated or added, not everything.

Incremental load is very useful, especially if your dataset has a large amount of data and it gets updated frequently. It greatly speeds up the data import process and saves resources because only recent changes in the scope of an already existing dataset are refreshed.

Performance: Incremental refresh is faster and has less impact on network, storage, and memory-committed quotas than full dataset refreshing. This reduces the load on your data source and the Power BI service, enabling faster refresh times as well as cost reductions.

For example, in the retail business with the same sales records, in incremental refresh, we only need to refresh the sales data for the new period (last month) and the previous data will not be touched. This method significantly reduces the time and resources required to refresh the dataset.

Understanding Incremental Refresh

Incremental refresh operates by leveraging Power BI Incremental Refresh parameters to filter and update data. The core concept is to refresh only a subset of data, typically the most recent records. This approach drastically reduces the time and resources required for data refreshes, making it feasible to work with large datasets. By setting Power BI Incremental Refresh parameters for a specific date range, Power BI can focus on updating only the relevant data, leaving the historical data unchanged.

Query Folding and Incremental Refresh

Query folding refers to the ability of Power Query in Incremental Refresh to push transformations back to the data source, allowing for efficient query execution. For incremental refresh to work optimally, the data source must support query folding. This ensures that the filtering logic (using RangeStart and RangeEnd) is executed at the data source level, minimizing data transfer and processing time.

When a query is folded, the operations, such as filters and aggregations, are translated into the native queries of the data source (e.g., SQL). This means that the heavy lifting is done by the data source, which is typically more powerful than the Power BI client.

Data Sources That Support Query Folding and Incremental Refresh

Not all data sources support query folding. However, many do, including:

• SQL Server: A widely used relational database management system.
• Azure SQL Database: A managed cloud database provided by Microsoft.
• Oracle: An enterprise-grade relational database.
• SAP HANA: An in-memory, column-oriented, relational database management system.
• Teradata: A massively parallel processing database.

When setting up incremental refresh, verify that your data source supports query folding to take full advantage of this feature. If your data source does not support query folding, incremental refresh may not work as efficiently.

The Limitations with Incremental Refresh

While incremental refresh is powerful, it has some limitations:

• Data Source Compatibility: Not all data sources support query folding. Without query folding, the efficiency gains of incremental refresh may be lost.
• Complex Queries: Queries with complex transformations might not fold, limiting incremental refresh effectiveness. In such cases, you may need to simplify your queries or adjust your data model.
• Initial Setup: Requires careful setup of parameters and filters. Incorrect configuration can lead to incomplete or incorrect data refreshes.
• Licensing: Incremental refresh is available only to Power BI Pro and Premium users. This limitation may require upgrading your license if you are on the free tier.

Conclusion

Incremental refresh in Power BI is a game-changer for managing large datasets efficiently. By refreshing only the data that has changed, it optimizes performance, reduces resource usage, and enhances scalability. This guide provided a comprehensive step-by-step approach to setting up incremental refresh, ensuring your data remains current and your reports run smoothly. Implementing incremental refresh can significantly improve your data management practices, making your Power BI environment more efficient and cost-effective. 

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FAQ

What is the difference between full refresh and incremental refresh?

Full refresh reloads the entire dataset from the data source, while incremental refresh updates only the new or modified data since the last refresh. Incremental refresh is more efficient for large datasets, as it reduces the amount of data processed during each refresh.

How do you refresh incremental data in Power BI?

To refresh incremental data, you need to set up parameters (RangeStart and RangeEnd), apply filters in Power Query to limit the data to the specified range, define incremental refresh policies in Power BI Desktop, and schedule the refresh in Power BI Service. This setup ensures that only the new or changed data is refreshed, optimizing the refresh process.

What is the difference between direct query and incremental refresh in Power BI?

While Direct Query pulls data right from the source every time a report is used, it offers real-time data but may have potential performance implications. On the other hand, Incremental Refresh updates data at regular intervals to keep updating recent changes and maintains a trade-off between the currency of data and performance indices. As a result, with Power BI, we only have two data preparation options: Direct Query, which is apt when real-time data is of utmost importance, and Incremental Refresh, where large-size datasets get updated regularly.

What are the different types of refresh in Power BI?

• Full Refresh: The whole dataset will be reloaded from the data source, taking care of updated and missing records.
• Incremental Refresh: Allows for only the new or changed data since the last refresh. This is possible with huge datasets.
• Direct Query: It allows extracting real-time or near-real-time data directly from the live source.
• Automatic Data Refresher: Refreshes the dataset at set time intervals (like daily, or weekly) to keep data up-to-date without manual verifications.

What is an incremental update?

An incremental update restores only the data that changed from the last download. For Power BI, this essentially refers to refreshing just the new records (or changes) to make updates quicker and more efficient. This would allow a lesser amount of data to be processed at each refresh and hence keep the processing requirements in check, maintaining desired performance levels for resources.