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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.”
Live Snowflake queries show current data but can’t track how metrics changed over time. Without historical snapshots, you lose the ability to analyze trends, compare performance periods, or maintain audit trails for compliance.
Here’s how to automatically capture and preserve historical Snowflake data in Google Sheets for comprehensive trend analysis and audit requirements.
Automate historical data capture with Snowflake snapshots using Coefficient
Coefficient’s Snapshots feature provides sophisticated historical data tracking by capturing entire datasets to new tabs with timestamps or appending historical data to designated areas for consolidated views. You can schedule snapshots to run hourly, daily, weekly, or monthly with retention management to control tab proliferation.
This functionality addresses the critical need for historical data tracking that Snowflake’s live queries alone cannot provide. You get automatic timestamp columns showing capture date and time, preserved data state at specific points in time, and clear historical records for compliance requirements.
How to make it work
Step 1. Import your Snowflake data using Coefficient’s connection.
Set up your initial data import from Snowflake using the direct connector. This becomes the foundation for your snapshot system.
Step 2. Configure snapshot settings in advanced options.
Access the import’s advanced settings and enable snapshots. Choose between full tab copies for complete historical records or targeted cell appends for consolidated trend views.
Step 3. Set your capture frequency based on business needs.
Configure daily snapshots of pipeline stages to analyze velocity, hourly captures for supply chain optimization, weekly snapshots of engagement metrics, or monthly closes for compliance requirements.
Step 4. Enable timestamp preservation and retention management.
Turn on automatic timestamp columns and set retention periods like keeping the last 30, 60, or 90 days. Configure automatic old snapshot removal to prevent storage bloat.
Step 5. Build trend analysis from historical snapshots.
Use the captured historical data for side-by-side comparisons, cohort analysis, conversion rate trends, and performance baselines. Create charts and pivot tables that track how specific records change over time.
Enable sophisticated historical analysis
A Rev Ops team can snapshot their sales pipeline every Monday, creating a 52-week rolling view of how deals progress through stages. This enables sophisticated cohort analysis, conversion rate trends, and sales velocity calculations that would be impossible with point-in-time queries alone.
Ready to build comprehensive historical data tracking for your Snowflake analytics? Start capturing automated snapshots with Coefficient today.
