How Long Does Power BI Take to Refresh?

A Power BI refresh that takes forever is more than just an inconvenience, it can create serious reporting delays and leave decision-makers waiting on critical data. The time it takes for a Power BI dataset to refresh can range from a few seconds to several hours, depending on several key factors. This guide will walk you through what affects your refresh speed, how to check its duration, and most importantly, how to speed it up.

What Determines How Long a Power BI Refresh Takes?

Understanding why your refresh is slow is the first step to fixing it. Refresh duration isn't random, it's a direct result of the work Power BI has to do. The total time is a sum of the time taken for Power BI to extract data from the source, transform it, and load it into the data model.

Here are the primary factors that influence this process:

1. Data Source Performance and Location

Where your data lives has a massive impact on refresh speed. A query to a high-performance, well-indexed SQL database will almost always be faster than pulling data from flat files or a sluggish web API.

• Databases (SQL Server, etc.): Typically the fastest, especially if they are located on the same network. Performance depends on the server's hardware and how efficiently your query is written.
• Flat Files (Excel, CSV): Refresh speed depends on the location (local, SharePoint, network drive) and file size. Reading large files from a slow network share can create significant bottlenecks.
• APIs and Web Services: These can be the most unpredictable. Their speed is limited by throttling (rate limits), network latency, and the performance of the service provider's servers.

2. The Volume of Data

This one is straightforward: more data equals longer refresh times. A dataset with 10 million rows will naturally take much longer to refresh than one with 10 thousand rows. Refreshing involves moving this data from the source to the Power BI service, and every row adds to the total processing time.

3. Complexity of Power Query Transformations

What you do to your data in Power Query before it's loaded into the model is one of the biggest contributors to refresh duration. Every transformation step you add requires processing power.

Complex operations can be particularly time-consuming:

• Merging and Appending: Combining multiple tables, especially large ones, uses a lot of memory and processing time.
• Unpivoting Columns: Transforming wide tables into long tables can be a heavy operation.
• Complex M Code: Custom functions, intensive string manipulation, or branching logic can dramatically slow down the transformation phase.
• Missing Query Folding: "Query Folding" is a powerful Power BI feature where transformation steps are translated into a single native query (like SQL) and sent to the data source. For example, filtering rows in Power Query on a SQL data source might get "folded" into a WHERE clause in the SQL query. When folding doesn't occur, Power BI is forced to download the entire table first and then perform the transformations in its own engine, which is much slower.

4. Data Model Structure and Calculated Columns

After your data is transformed, it gets loaded into the data model. The structure of this model matters.

• Calculated Columns: Unlike measures, which are calculated on-the-fly when you interact with a report, calculated columns are computed during the refresh process and stored in your model. Every single row needs to be calculated, which can add significant time for large tables with complex DAX formulas.
• Relationships and Model Complexity: A highly complex model with many intricate relationships can increase the processing time required to load and compress the data. Simple star schema models (a central fact table connected to multiple dimension tables) are generally the most efficient.

5. On-Premises Gateway Performance

If your data sources are located on-premises (e.g., a local SQL server), you need a data gateway to securely connect them to the cloud-based Power BI service. The gateway acts as a bridge, and its performance is critical.

• Server Hardware: The gateway machine needs adequate CPU, RAM, and network bandwidth to handle data transfer, especially if multiple datasets are refreshing simultaneously.
• Network Latency: The physical distance and network path between your gateway and the data source, as well as between your gateway and the cloud, can introduce delays.

6. Power BI Capacity and License Type

The type of Power BI license you have determines the computational resources allocated to your refreshes. This is a hugely important, and often overlooked, factor.

• Power BI Pro (Shared Capacity): With a Pro license, your workspace runs on shared resources with other Microsoft tenants. If many tenants are running refreshes at the same time, you may experience throttling or slower, less predictable performance. Pro has a refresh-per-day limit and a maximum dataset size limit.
• Power BI Premium (Dedicated Capacity): Premium provides dedicated, isolated resources for your organization. This leads to more consistent, reliable, and faster refresh times because you aren't competing with anyone else for processing power. Premium also offers more refreshes per day and access to powerful features like Incremental Refresh.

How to Check Your Refresh Duration

Before you can optimize, you need to know where you stand. Power BI makes it easy to see how long your refreshes are taking and whether they are succeeding or failing.

1. Navigate to the Power BI service (app.powerbi.com).
2. Go to the workspace that contains your dataset.
3. Hover over the dataset (not the report) and click the ellipsis (...).
4. Select Settings, and then go to the Refresh history tab.

Here you'll see a log of all recent refreshes (both scheduled and on-demand). You can review the Start time, End time, and Duration for each entry, as well as its status (e.g., "Completed" or "Failed"). If a refresh fails, this is where you'll find an error code and message to help you troubleshoot.

Best Practices for Faster Power BI Refreshes

Now for the actionable advice. Here are proven strategies to get your refresh times down.

1. Optimize in Power Query First

The Power Query Editor is your first line of defense against slow refreshes. The goal is to make the data as clean and lean as possible before it ever reaches your data model.

• Filter Early and Often: The single most effective optimization is to remove data you don’t need. Add filter steps (e.g., Table.SelectRows) as early as possible in your list of applied steps. This can trigger query folding and reduces the amount of data Power BI has to process in all subsequent steps.
• Remove Unnecessary Columns: If your report doesn't use a column, remove it. Use the "Choose Columns" feature instead of manually removing columns one by one. Fewer columns mean less data to move and store.
• Ensure Query Folding is Happening: Right-click a transformation step in Power Query and see if the "View Native Query" option is available. If it is, folding is working for that step. If it grays out at a certain point, that step broke folding, meaning all subsequent steps are processed by Power BI's own engine. Try to reorder or rewrite steps to maintain folding as long as possible.
• Be Mindful of Data Types: Power BI is more efficient with numerical and date types than with text. Make sure your data types are set correctly and are as precise as needed.

2. Implement Incremental Refresh (Game Changer)

For large datasets updated periodically, incremental refresh is a revolutionary feature. Instead of wiping and reloading the entire dataset every time, it allows you to refresh only the most recent data.

To set it up:

1. You first create two date/time parameters in Power Query named RangeStart and RangeEnd.
2. You apply these parameters to filter your main data table (typically on an order date or modification date column).
3. In the Power BI service, you configure the incremental refresh policy on the dataset, defining how much historical data to store and how often to refresh the newest "slice" of data.

This single technique can reduce a multi-hour refresh down to a few minutes. Note that incremental refresh is a Power BI Premium feature.

3. Build an Efficient Data Model

A smart data model not only enables powerful analysis - it also refreshes faster.

• Use Measures Instead of Calculated Columns: This is a golden rule. Calculated columns can bloat your model and kill refresh performance. Whenever possible, create a measure using DAX instead. Measures don't get calculated during refresh, they are evaluated on the fly when a user interacts with a report.
• Embrace a Star Schema: Instead of loading one massive, wide table, structure your model with a central fact table (containing numbers and keys) connected to smaller dimension tables (containing descriptive attributes). This is the gold standard for both performance and usability in BI.
• Minimize Cardinality: High-cardinality columns (like unique IDs or timestamps with milliseconds) require more memory to store and process. If you don't need that level of detail, consider removing the column or rounding the values to reduce duplicates.

4. Upgrade Your Hardware and Capacity

Sometimes, the easiest fix is simply moving to a more powerful environment.

• Gateway Server: If you're using an on-premises gateway, ensure the machine it's installed on is not under-resourced. Consider upgrading its CPU and RAM or moving it to a dedicated server.
• Power BI Capacity: If your organization consistently struggles with slow refreshes across many datasets, it may be time to move from Pro to Premium Per User (PPU) or a full Premium capacity. The dedicated resources provide a more stable and high-performance environment that eliminates the "noisy neighbor" problem of shared capacity.

Final Thoughts

Optimizing Power BI refresh times is a process of identifying and addressing bottlenecks across your entire data pipeline - from the source to the data model. By diligently applying transformation best practices, structuring an efficient model, leveraging features like incremental refresh, and ensuring you have adequate hardware resources, you can turn sluggish refreshes into quick, reliable updates.

The manual drudgery of building reports, waiting on slow refreshes, and tweaking complex BI tools is why we built Graphed. We connect to your marketing and sales data sources (like Google Analytics, Salesforce, and Shopify) and keep everything updated in real-time. Instead of spending hours in Power Query or rebuilding pivot tables, you can just ask a question in plain English to instantly create live dashboards and get immediate answers, freeing up your team to focus on insights, not setup.