Does Incremental Refresh Work in Power BI Desktop?

Dealing with large datasets in Power BI can make report refreshes feel excruciatingly slow. Incremental refresh is the solution designed to speed things up, but there's a common point of confusion: does it actually work in Power BI Desktop? This article will give you the clear answer and walk you through every step of the setup process.

What Exactly is Incremental Refresh?

Before we dive into the "how," let's quickly cover the "what." A standard data refresh in Power BI is a full refresh. It deletes all existing data from your table and re-imports everything from the source. When your table has millions or even billions of rows, this process consumes a massive amount of time and resources, both on Power BI's end and on your source system.

Incremental refresh is a much smarter approach. Instead of wiping and replacing everything, it only refreshes a small, recent slice of your data - the part that's actually changing. It partitions your table into historical "cold" data that rarely, if ever, needs to be updated, and recent "hot" data that gets refreshed regularly.

Think of it like building with LEGO bricks:

• Full Refresh: Smashing your entire LEGO castle to bits and rebuilding it from scratch every single day.
• Incremental Refresh: Keeping the existing castle and just adding the new bricks that were delivered today.

The benefits are significant:

• Faster Refreshes: You're only processing a fraction of the data, which can take refresh times from hours to minutes.
• Reduced Resource Consumption: It's less demanding on the Power BI service and your source database (e.g., SQL Server), leading to more reliable refreshes that are less likely to time out.
• Near Real-Time Data: For dashboards using DirectQuery, you can use the setting "Get the latest data in real time with DirectQuery," which maintains high performance while still showing the most recent updates.

The Short Answer: No, Not in the Way You Think

So, does incremental refresh work in Power BI Desktop? The straightforward answer is no, not really. Power BI Desktop is a development tool, not a live service. While you must configure the incremental refresh policy within Power BI Desktop, the actual partitioning and refreshing mechanism only activates once the report is published to the Power BI Service.

This is a critical distinction that trips up a lot of new users. The setup happens in Desktop, but the execution happens in the cloud.

What Does Happen in Desktop?

When you set up incremental refresh in your .PBIX file, the parameters you create (RangeStart and RangeEnd) act as a simple filter inside Power BI Desktop. The report will only load the data that falls between the dates you initially define for those parameters. This is actually a feature, not a bug. It prevents you from having to load a 100-million-row table into your local machine just to build a new visual, dramatically speeding up your development workflow.

However, running a manual refresh in Power BI Desktop will not trigger the incremental refresh logic. It will still perform a full (but filtered) refresh of the data defined by your parameter range.

How to Set Up Incremental Refresh in Power BI Desktop

Even though the magic only happens in the service, you have to complete the entire setup in Power BI Desktop before publishing. Here’s a step-by-step guide.

Step 1: Create 'RangeStart' and 'RangeEnd' Parameters

First, you need to define two special date/time parameters that Power BI will use to filter your data. The names are case-sensitive and must be spelled exactly as follows:

• RangeStart
• RangeEnd

1. In Power BI Desktop, go to the Home tab and click Transform Data to open the Power Query Editor.
2. Inside the Power Query Editor, click Manage Parameters > New Parameter.
3. Create the RangeStart parameter with the following settings:

• Name: RangeStart
• Description: (Optional, but helpful) Start date for the incremental refresh range.
• Type: Date/Time
• Suggested Values: Any
• Current Value: Enter a start date for the data you want to load into your Desktop file, for example, 1/1/2023 12:00:00 AM.

1. Click New and create the RangeEnd parameter:

• Name: RangeEnd
• Description: (Optional) End date for the incremental refresh range.
• Type: Date/Time
• Suggested Values: Any
• Current Value: Enter an end date, for example, 12/31/2023 12:00:00 AM. This range determines how much data you work with locally during development.

You should now have two new parameters listed in the Queries pane.

Step 2: Filter Your Table Using the Parameters

Next, you'll apply these parameters as a filter to the date column in the table you want to incrementally refresh.

1. In the Power Query Editor, select the table you are configuring (e.g., 'Sales').
2. Find your primary date column (e.g., 'OrderDate'). Click the filter arrow on the column header.
3. Go to Date/Time Filters > Custom Filter...
4. Configure the filter as follows:

• Show rows where 'OrderDate' a. is after or equal to the RangeStart parameter.
• AND b. is before the RangeEnd parameter.

Important Tip: Use is before for RangeEnd, not is before or equal to. This ensures you only refresh complete periods. For instance, if you refresh at 8 AM, you avoid loading a partial, incomplete day's data.

1. Click OK. Then, in the Home tab, click Close & Apply.

Power BI Desktop will now apply this filter and your report view will update to show only the data within the RangeStart and RangeEnd dates you specified.

Step 3: Define the Incremental Refresh Policy

Now, exit the Power Query Editor and return to the main report view. This is where you set the actual refresh and archive rules.

1. In the Fields pane on the right, find your table. Right-click on it and select Incremental Refresh.
2. The Incremental Refresh dialog box will appear. Here’s how to configure it:

• Toggle incremental refresh on: At the top, slide the toggle to On.
• Archive data starting: Define your "cold" storage period. This is the bulk of your historical data that won't get refreshed. For example, store data for the last 5 Years before the refresh date.
• Incrementally refresh data starting: Define your "hot" data window. This is the recent slice that Power BI will refresh regularly. For example, refresh data in the last 10 Days before the refresh date.

1. (Optional but recommended) You can enable additional settings:

• Detect data changes: If you have a specific last_modified_date column in your data source, you can use it to intelligently refresh only the periods where data has actually changed, making refreshes even more efficient.
• Only refresh complete days/months: This useful setting prevents partial period data from being imported.

1. Click Apply. You may see a warning that you won't be able to download the .PBIX file from the service once it contains partitions. This is normal, just click OK.

Step 4: Publish to the Power BI Service

Your work in Desktop is done! The final step is to publish your report to a workspace in the Power BI Service that has a Premium, Premium Per User (PPU), or Embedded capacity. Incremental refresh is a premium feature and will not work in a standard Pro workspace.

What Happens After You Publish

Once your report is in the Power BI service, the magic finally begins.

The very first time you refresh the dataset in the service, Power BI performs a full data load. It ignores the small date range you set in Desktop and loads data for the entire archive and refresh period you defined in the policy (e.g., 5 years + 10 days). During this initial refresh, it creates all the necessary partitions (e.g., yearly, quarterly, monthly, daily partitions depending on your data volume and policy).

Every subsequent scheduled refresh is now incremental. Power BI will only refresh the partitions that fall within your defined refresh window (the last 10 days in our example). The historical 5 years of data will be left untouched, making the operation incredibly fast and efficient.

Final Thoughts

In summary, incremental refresh must be configured entirely within Power BI Desktop, but it only becomes operational after you publish your report to a Premium, Premium Per User (PPU), or Embedded workspace in the Power BI service. Desktop simply uses your parameters to filter the data, which creates a lighter-weight and faster environment for development, a useful feature in itself.

We know that managing data pipelines and configuring complex settings in tools like Power BI can be time-consuming. That's why we built Graphed to streamline the entire process. Rather than setting up parameters and policies, our AI data analyst connects directly to your data sources like Google Analytics, Shopify, and Salesforce. From there, you simply ask for what you want in plain English, and Graphed automatically builds and maintains live, interactive dashboards for you, no complex configuration required.