Power Automate: Excel Slowing Your Flows

Key insights

• Excel bottleneck: Writing rows to Excel one-by-one—rather than the SharePoint service or Graph API—causes slow exports.
  One-by-one writes create many small operations that add minutes or hours to a flow instead of seconds.
• Bulk insert with Office Scripts: Use an Office Script that accepts a full set of rows and writes them in one operation.
  This approach cuts execution time from minutes to seconds by reducing round trips and per-row overhead.
• JSON array and payload size: Build a JSON array of row objects in Power Automate and pass it to the Office Script.
  Watch flow size limits and memory; if the array is large, split it into chunks to avoid timeouts or failures.
• ChatGPT to create scripts and Power Automate flow integration: Generate a bulk-insert Office Script (or adapt a template) and add the "Run script" action in your flow.
  Map the JSON parameter to the script, then test mapping of headers and types before full runs.
• Testing and monitoring: Run small tests, measure run-time improvements, and add logging (console.log) inside the Office Script to trace issues.
  Include error handling, retries, and validate row counts after each batch.
• SharePoint admins and best practices: Use bulk insert for large exports like audit logs or reports to avoid throttling-like symptoms.
  Keep headers consistent, consider Dataverse or CSV staging for extremely large datasets, and limit concurrent flows to protect service reliability.

Overview

In a recent YouTube walkthrough, Alireza Aliabadi demonstrates why the perceived slowdowns when exporting data from SharePoint to Excel often come from the way rows are written, not from external throttling or limits on the Microsoft Graph. He shows that writing rows one at a time in a Power Automate flow becomes the hidden bottleneck, and that switching to a bulk insert approach can cut execution time from minutes to seconds. The video guides viewers through building a JSON array, creating an Office Scripts script (with help from ChatGPT), and wiring everything back into a flow for much faster exports.

The Root Cause: Row-by-Row Writes

Aliabadi explains that many admins instinctively blame throttling, API ceilings, or SharePoint itself when exports drag on, but the real issue is the per-row write pattern. Each individual write generates overhead: authentication checks, request latency, and workbook processing time, which compounds when thousands of rows are processed. Consequently, the flow spends most of its time in many small operations rather than in efficient bulk transfer.

Bulk Insert Strategy with Office Scripts

The alternative Aliabadi presents leverages an Office Scripts routine that accepts a structured JSON array and writes rows in a single operation inside the Excel context. This approach reduces round trips between Power Automate and the workbook and lets the script use native Excel APIs like setValues to populate ranges quickly. He demonstrates generating the script with assistance from ChatGPT, which speeds development, and then calling it from the flow to process many rows at once.

Tradeoffs and Limits

While bulk inserts significantly improve speed, Aliabadi notes several tradeoffs that admins must balance. Large JSON payloads can hit service size limits or increase memory use during runtime, so flows often need to chunk data into manageable batches; thus, you trade a single large operation for several medium ones. Moreover, executing scripts with large datasets places more load in the Excel runtime, which may trigger timeouts or require careful error handling and logging to maintain reliability.

Technical Challenges and Best Practices

The video also outlines practical challenges such as schema alignment, data validation, and concurrency when multiple flows access the same workbook. To reduce risk, Aliabadi recommends mapping fields carefully when building the JSON array and adding incremental retries and checkpoints so partial failures can be recovered without reprocessing everything. Additionally, he highlights monitoring and testing the approach with realistic export sizes to tune batch sizes and retry policies.

Security, Maintenance, and Automation Tradeoffs

Using ChatGPT to scaffold an Office Scripts script speeds development but introduces maintenance and security considerations that teams must address. Generated code often needs review and adjustments to match organizational authentication patterns, error handling conventions, and compliance requirements, so relying on AI as a starting point is useful but not a substitute for careful code review. In practice, teams balance developer speed against the need for audited, maintainable scripts.

Operational Recommendations

Aliabadi’s method includes several operational recommendations: build and test a JSON payload sample first, tune batch sizes based on observed runtime and memory, and instrument flows with clear logging so failures are visible and recoverable. He also suggests scheduling exports during off-peak hours when possible and using chunking to stay within action size limits while still benefiting from bulk writes. These measures help reconcile speed improvements with robustness and predictable operations.

When Bulk Insert Might Not Fit

Despite its advantages, the bulk insert approach is not universally optimal, especially for very large datasets or highly concurrent environments where multiple processes update the same workbook. In such cases, shifting to a database-backed pipeline, exporting to CSV, or using a data warehouse may offer better scalability and simpler concurrency control. Therefore, admins should evaluate the dataset size, expected concurrency, and downstream reporting needs before committing to a single solution.

Impact for SharePoint Admins and Reporters

For SharePoint admins who regularly export audit logs, reporting data, or large lists, the bulk insert pattern delivers clear time savings and can transform slow, manual workflows into near real-time exports. Aliabadi’s demonstration provides a reproducible pattern that reduces flow run times dramatically while also giving admins the tools to test limits and make informed tradeoffs. Consequently, teams can move from blaming external services to optimizing their own automation design.

Conclusion

Alireza Aliabadi’s video reframes a common performance problem by pinpointing how per-row writes to Excel amplify latency and offering a practical bulk-insert solution with Office Scripts. While the approach delivers major speed gains, it requires careful batching, validation, and monitoring to avoid size and runtime limits. Overall, the technique is a useful addition to an admin’s toolkit, but it should be applied thoughtfully alongside alternatives when scale or concurrency demands grow.

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