)
Written by
Table of Contents
“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.”
Large dataset handling and pagination are major limitations when attempting to export HubSpot data to Excel through workflows, which process records individually and struggle with API rate limits and memory issues.
Here’s an enterprise-grade solution that handles large datasets automatically with robust pagination management and optimized performance for datasets that would timeout in workflow scenarios.
Handle enterprise-scale datasets without pagination complexity using Coefficient
CoefficientHubSpotspecifically addresses large dataset challenges with robust support for 50,000+ rows, automatic pagination handling, and optimized data transfer for largedatasets without any manual configuration required.
The system includes batch processing, resume capability for interrupted imports, and memory management that prevents the performance issues common with large workflow-based exports.
How to make it work
Step 1. Configure HubSpot import with field selection to optimize data size.
Connect to HubSpot and select only the fields you need for your large dataset export. This reduces data transfer size and improves performance for enterprise-scale imports like complete contact databases with 100k+ contacts.
Step 2. Apply server-side filtering to reduce data transfer.
Use Coefficient’s filtering system to apply criteria before data transfer, reducing the dataset size at the source. This smart filtering minimizes network overhead and improves performance for large historical datasets.
Step 3. Set up appropriate refresh schedules based on dataset size.
Configure refresh timing that accounts for your dataset size and update frequency needs. Monitor import performance through the Connected Sources dashboard to track large import status and optimize scheduling.
Step 4. Use snapshots for historical preservation of large datasets.
Create scheduled snapshots to preserve large historical datasets while your main import continues refreshing. This handles enterprise scenarios like multi-year activity tracking across all objects without performance degradation.
Handle enterprise datasets without technical complexity
Start managingEnterprise-grade handling of large datasets eliminates the pagination issues inherent in workflow-based approaches, with no technical development required and better performance than custom API implementations.your large datasets today.
