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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
HubSpot’s API rate limits of 100 requests per 10 seconds create significant bottlenecks when importing large volumes of user event data from product analytics tools.
Here’s how to manage these constraints using scheduled batch processing that respects rate limits while maintaining data freshness.
Distribute API calls over time with scheduled batch processing
Coefficient ‘s scheduling capabilities provide an elegant solution for managing API rate limits during bulk imports. Rather than overwhelming HubSpot ‘s API with continuous requests, you can batch process user event data through controlled intervals.
How to make it work
Step 1. Import user event data in scheduled batches.
Set up scheduled imports from your analytics tools (hourly, daily, or custom intervals) that pull manageable chunks of data. This prevents overwhelming your processing capacity and stays within reasonable API usage patterns.
Step 2. Process and transform data in API-appropriate batches.
Use spreadsheet logic to organize your data into batches that align with HubSpot ‘s rate limits. Calculate optimal batch sizes based on your data volume and required processing frequency.
Step 3. Schedule exports to stay within rate limits.
Configure Coefficient’s scheduled exports to push data to HubSpot in controlled intervals. Space out your exports to distribute API calls over time rather than hitting limits with burst activity.
Step 4. Implement data queuing for high-volume periods.
Create overflow handling in spreadsheets where excess data waits for the next processing window. Use formulas to prioritize critical events and ensure important data gets processed first during capacity constraints.
Step 5. Monitor processing status and handle errors gracefully.
Track which batches have been processed and identify any failures. Coefficient’s error handling ensures failed exports can be retried without losing data, which is crucial when working within API constraints.
Scale your data imports without hitting API walls
This method distributes API calls over time, prevents rate limit violations, and provides visibility into data processing status. Start optimizing your bulk import process today.
