Power Automate is a powerful tool for workflow automation, but handling large data sets presents challenges like delegation limits, performance issues, and API restrictions. However, with the right strategies, you can efficiently process thousands or even millions of records in Power Automate.
In this article, we’ll explore:
Challenges of managing large data sets in Power Automate
Best practices for handling large volumes of data
Techniques to improve performance and overcome delegation issues
Real-life examples of managing large data sets
1. Challenges of Handling Large Data Sets in Power Automate
Delegation Limits: Some actions in Power Automate retrieve only 256 or 5000 records by default, depending on the connector.
API Request Limits: Many services (like SharePoint, SQL, and Dataverse) limit the number of API calls per minute.
Slow Performance: Processing large data sets in a single flow run can cause delays and timeouts.
Looping Constraints: Iterating through thousands of records using “Apply to Each” can be inefficient.
Data Source Restrictions: Some databases and cloud services throttle connections, affecting data retrieval speeds.
2. Best Practices for Managing Large Data Sets
Use Pagination in Power Automate
Pagination helps retrieve large data sets in smaller batches.
How to Enable Pagination:
- When using “List rows (Dataverse)”, “Get Items (SharePoint)”, or other connectors, go to “Settings” and enable pagination.
- Set a maximum record limit (e.g., 100,000 records for Dataverse).
Impact:
Retrieves large data sets efficiently
Reduces API throttling issues
Process Data in Batches Instead of One Large Request
Instead of processing all records at once, split data into chunks using loops or filters.
Example: Processing 10,000 records in batches of 500
1️⃣ Use “List rows (Dataverse)” with pagination to fetch 500 records per batch
2️⃣ Process each batch separately using “Apply to Each”
3️⃣ Use “Delay” action if needed to prevent API throttling
Impact:
Prevents timeouts
Improves flow performance
Use Parallelism for Faster Data Processing
By default, Power Automate runs loops sequentially, which slows down performance. Enable concurrency control to process multiple records in parallel.
How to Enable Parallelism:
- In the “Apply to Each” action, enable Concurrency Control
- Set a value like 20 or 50 for faster processing
Impact:
Reduces processing time significantly
Speeds up data handling
Use Filter Queries to Reduce Data Load
Fetching all records at once can slow down Power Automate. Instead, use OData filters to fetch only the required records.
Example: Filtering SharePoint List items with a status of “Pending”
Use Filter Query in the “Get Items” action:
Status eq 'Pending'
Impact:
Retrieves only relevant data, improving performance
Reduces unnecessary API calls
Store Processed Data in a Staging Table
Instead of handling all records in Power Automate, store data in a staging table (SQL, Dataverse, or SharePoint) and process it incrementally.
How to Implement:
1️⃣ Fetch data from a source system
2️⃣ Store the data in Dataverse or SQL Server
3️⃣ Process incremental changes instead of reprocessing all records
Impact:
Reduces duplicate processing
Improves flow efficiency
Use Azure Logic Apps for Large Data Workflows
If Power Automate struggles with large-scale data processing, Azure Logic Apps can handle bigger workloads.
Benefits of Logic Apps:
✔️ No record limits
✔️ Handles millions of records
✔️ More control over batch processing
Example Use Case:
Automating data migration from SharePoint to SQL with millions of records using Logic Apps instead of Power Automate.
3. Real-Life Examples of Handling Large Data Sets
Example 1: Processing 100,000 SharePoint List Items
Problem: A company needed to update 100,000+ SharePoint items daily but faced performance issues.
Solution:
✅ Enabled pagination to fetch records in batches of 5000
✅ Used parallel processing in “Apply to Each”
✅ Applied Filter Query to fetch only modified records
Result: Reduced flow run time from 3 hours to 20 minutes!
Example 2: Syncing Large Dataverse Tables to SQL Server
Problem: A business had to sync millions of records from Dataverse to SQL Server, but API limitations slowed down the process.
Solution:
✅ Used incremental updates instead of syncing all records
✅ Implemented batch processing (500 records per request)
✅ Moved to Azure Logic Apps for better scalability
Result: Improved sync time by 80% and reduced API throttling issues.
Example 3: Automating Invoice Processing for Thousands of Customers
Problem: A finance team needed to process thousands of invoices from an API and store them in Dataverse.
Solution:
✅ Used Filter Query to fetch only unprocessed invoices
✅ Enabled pagination to retrieve 5000+ records
✅ Stored processed data in a staging table before final processing
Result: Successfully automated thousands of invoices daily without hitting API limits.
4. Key Takeaways
✅ Use pagination to fetch large records in batches
✅ Enable concurrency to speed up processing
✅ Use OData filters to retrieve only necessary data
✅ Break down large workflows into smaller, manageable parts
✅ Consider Azure Logic Apps for heavy workloads
With these best practices, Power Automate can efficiently handle large data sets, ensuring smooth automation and scalability for businesses.