Efficient data management in Power Query often involves choosing between different methods for loading and processing data. In a recent breakdown by Excel Off The Grid, two approaches were tested to compare their impact on performance: creating separate connections for each sheet in an Excel workbook versus referencing a single connection multiple times. Using a controlled test environment with a workbook containing 10 sheets of 10,000 rows each, the analysis revealed that Power Query’s robust caching mechanism minimizes performance differences between these methods. For example, the average refresh times differed by only 0.09 seconds, highlighting the negligible impact of the chosen approach on overall efficiency.
Explore how these findings apply to various scenarios, such as working with larger datasets, using the Table.Buffer function, or loading data from external sources like SharePoint. You’ll also gain insight into how file size and network latency influence refresh times and why Power Query’s caching ensures consistent performance across workflows. By the end, you’ll be equipped to design workflows that balance clarity and maintainability without compromising on efficiency.
Connections vs References in 2026
TL;DR Key Takeaways :
- Power Query’s file caching mechanism ensures minimal performance differences between creating separate connections for each sheet and referencing a single connection multiple times.
- Testing with a 12.3 MB Excel workbook containing 10 sheets showed negligible refresh time differences: 4.43 seconds for separate connections vs. 4.34 seconds for single connection references.
- Additional tests revealed that using
Table.Bufferor loading data from multiple workbooks did not significantly improve performance, with refresh times increasing when reading from multiple files. - Even with larger datasets (up to 1 million rows) or external sources like SharePoint, the performance gap between the two methods remained negligible, highlighting Power Query’s adaptability.
- The choice between multiple connections and references should prioritize workflow clarity and maintainability, as performance differences are insignificant in most scenarios.
How the Test Was Conducted
To evaluate performance, a controlled test environment was established. The test involved an Excel workbook containing 10 sheets, each with 10,000 rows of data, resulting in a total file size of approximately 12.3 MB. Two distinct methods were analyzed:
- Method 1: Creating separate Power Query connections for each sheet.
- Method 2: Creating a single connection to the workbook and referencing it multiple times for each sheet.
The primary goal was to measure and compare the average refresh times for each method, identifying whether one approach offered a clear performance advantage over the other.
Key Findings: Performance Results
The results of the tests revealed only minor differences in performance between the two methods. The average refresh times were as follows:
- Separate connections for each sheet: 4.43 seconds.
- Single connection with multiple references: 4.34 seconds.
The difference of just 0.09 seconds is negligible, indicating that Power Query’s file caching mechanism effectively optimizes the refresh process. This suggests that both methods perform similarly, allowing users to choose the approach that best suits their workflow without worrying about significant performance trade-offs.
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Exploring Additional Scenarios
To provide a more comprehensive analysis, additional tests were conducted under varying conditions. These scenarios offered deeper insights into how different factors influence Power Query performance:
- Using
Table.Buffer: Employing theTable.Bufferfunction to store data in memory showed no significant improvement, with an average refresh time of 4.41 seconds. This suggests that caching mechanisms already handle memory optimization effectively. - Loading from multiple workbooks: When data was loaded from 10 separate Excel workbooks instead of a single workbook, refresh times increased to an average of 5.97 seconds. This highlights the additional overhead introduced by reading from multiple files.
- Larger datasets: Testing with workbooks containing up to 1 million rows resulted in slower refresh times overall. However, the performance gap between the two methods (multiple connections vs. references) remained negligible, even with larger datasets.
- SharePoint as a data source: Loading data from SharePoint introduced higher refresh times due to network latency and server processing. Despite this, the choice between connections and references had little impact on performance in this scenario, reaffirming the consistency of Power Query’s caching mechanism.
These findings underscore the adaptability of Power Query across different use cases, demonstrating that its performance remains stable regardless of the chosen method or data source.
What This Means for Your Workflow
The results of these tests highlight the efficiency of Power Query’s file caching mechanism in optimizing refresh times. Whether you opt to create separate connections for each sheet or reference a single connection multiple times, the performance difference is minimal. This allows you to focus on designing workflows that prioritize clarity, maintainability and alignment with your organizational needs.
Additionally, strategies such as using Table.Buffer or reducing file reads may not yield significant performance improvements in most scenarios. For larger datasets or external sources like SharePoint, refresh times will naturally increase due to factors such as file size or network latency. However, the choice between multiple connections and references remains inconsequential, allowing you to prioritize workflow simplicity without sacrificing efficiency.
By understanding these performance dynamics, you can confidently tailor your Power Query workflows to suit your specific requirements, knowing that the underlying caching mechanisms will ensure reliable and consistent performance.
Media Credit: Excel Off The Grid
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