Power Automate: Update Excel Rows Fast

Key insights

• Problem: Updating Excel rows with Apply to each + Update a row can be very slow because the flow opens many separate connections to Excel for each row.
  These repeated calls create the main performance bottleneck.
• Efficient solution: Use Office Scripts to batch updates and call the script from Power Automate, which reduces per-row connection overhead.
  Example: a 100-row update went from ~80 seconds to under 5 seconds when converted to an Office Script call.
• How to implement: Record or generate an Office Script that updates rows in a named table, then add an action in Power Automate to call the Office Script.
  Recording a script or using a helper (like ChatGPT) speeds development and ensures the script handles rows in bulk.
• Key Power Automate actions and setup: Convert ranges to named tables, use a unique key column to match rows, and prefer List rows present in a table + filtering over per-row updates.
  Use Filter Array to find matches and only send batched work to the script.
• Performance tips and limits: Favor batch processing to avoid many separate Excel connections; this reduces API calls and timeouts.
  For very large datasets consider chunking or alternative storage, as flows can hit practical limits (commonly cited for moderate datasets).
• Practical testing and best practices: Test execution time after changes, map fields dynamically in the flow, and use Add a row into a table for non-matching records.
  Also set explicit table start rows/columns when importing unstructured content to avoid mapping errors.

Alireza Aliabadi’s recent YouTube video, titled "How to Update Excel Rows from Power Automate (The Efficient Way)," examines a common automation bottleneck and proposes a faster approach. In the video he demonstrates why typical methods can be slow and then shows how to speed them up using Office Scripts. Overall, the presentation combines a real-world timing example with practical steps for rebuilding a flow.

Overview of the Issue

The video starts by showing a real flow that updates 100 rows in about 80 seconds and then contrasts it with a rewritten solution that finishes in under five seconds. Thus, the core message is clear: the default per-row update pattern can be inefficient. Aliabadi frames the problem as a mixture of connector overhead and repeated round-trips to the file.

To make this concrete, he inspects a Power Automate flow that uses List rows present in a table, Apply to each, and Update a row actions. He explains that each update action opens a new connection to the workbook, which costs time when repeated. Consequently, flows that look simple at the surface can be surprisingly slow at scale.

Moreover, he emphasizes the typical triggers and sources that lead teams to this pattern, including SharePoint item events, scheduled syncs, and other automated feeds. Therefore, many organizations encounter the same problem when they attempt to push or sync dozens or hundreds of records. By demonstrating the timing, he makes the performance gap tangible for viewers.

Why Standard Updates Are Slow

Aliabadi breaks down what happens behind the scenes when a flow updates Excel rows one at a time and reveals the main cause: connection overhead. Each call to Update a row initiates authentication and file access, even when the file is already open in the session. As a result, repetitive actions generate latency that adds up quickly.

He also notes constraints such as service limits and file locking that can further degrade throughput when many updates run in sequence. Therefore, the pattern of "list, loop, update" hits practical ceilings for moderate and large datasets. In short, the connector design favors safe, discrete operations over bulk speed by default.

To quantify the difference, the video measures execution times before and after optimization and confirms dramatic improvement with the alternative method. This empirical approach helps viewers decide whether the extra effort to change the flow will justify the performance gains. Consequently, the timing data anchors the rest of the tutorial.

The Office Scripts Solution

As an alternative, Aliabadi introduces Office Scripts, which let you run JavaScript-based automation directly inside an Excel workbook. He records a script that performs the required updates in bulk and then calls that script from Power Automate. This design reduces the number of round-trips because the heavy lifting happens inside the workbook process.

He also demonstrates two practical ways to create the script: recording actions inside Excel and leveraging an AI-assisted draft from ChatGPT as a template. Then he shows how to wire that script into a flow, passing data as parameters so the workbook updates in a single execution. As a result, the optimized flow drops from tens of seconds to a few seconds for the same workload.

Importantly, the video includes a simple performance test so viewers can reproduce the results in their environment. He runs timing checks and compares the original and scripted approaches side by side. This empirical testing provides a repeatable method for teams to validate gains before deploying changes.

Tradeoffs and Practical Challenges

While the Office Scripts approach speeds execution, Aliabadi highlights tradeoffs that teams must weigh carefully. For example, scripts require additional setup and a degree of scripting knowledge, so they raise the bar for maintainability compared with native actions. Therefore, organizations must balance speed against long-term manageability.

Security and permissions slightly complicate the picture because scripts run in the context of the workbook and require appropriate access to OneDrive or SharePoint. Additionally, large or complex scripts may need version control and testing practices that teams must adopt. Thus, the faster path introduces operational overhead that teams should plan for.

Finally, he points out limits such as file size, row counts, and platform-specific behaviors that can still affect performance or cause failures. Therefore, it remains important to test with realistic datasets and error scenarios. In practice, a mixed approach—using native actions for small updates and scripts for bulk operations—often works best.

Recommendations and Next Steps

Aliabadi closes with practical advice for practitioners who want to improve Excel update performance in Power Automate. First, he recommends profiling current flows to measure baseline times so you can quantify improvements. Then he advises converting frequent bulk updates to Office Scripts when the volume and latency justify the transition.

He also suggests keeping a unique key column in tables to simplify matching and to build defensive checks into scripts for error handling. In addition, performing staged rollouts and automated tests helps prevent regressions in production. Consequently, teams can gain speed without sacrificing reliability.

Overall, the video offers a clear, practical path for teams that struggle with slow Excel updates in Power Automate. By demonstrating both the problem and a tested solution, Alireza Aliabadi gives viewers the information they need to choose the right approach for their environment. Consequently, teams can make informed tradeoffs between development effort and runtime performance.

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