Use incremental refresh with Snowflake
Snowflake experiments can use incremental refresh to reuse intermediate results across updates. GrowthBook scans new assignments and metric facts using the previous update's recorded timestamps, then adds those rows to intermediate warehouse tables. You can start with one compatible experiment before applying incremental refresh more broadly.
Start with selected experiments
Incremental refresh is in beta on Enterprise plans. You can opt selected experiments into incremental refresh while retaining ephemeral mode as your data source's default. This lets you evaluate the pipeline on one compatible experiment first.
Check the data pipeline
GrowthBook needs a write-enabled destination schema and intermediate tables retained beyond 24 hours. Before starting, check that:
- Your experiment uses metrics defined from fact tables.
- Assignment and fact sources are partitioned on timestamp.
- Metric conversion windows include in-progress conversions.
This beta does not support activation metrics or dimension breakdowns in Experiment Dashboards. Restated data and late arrivals with older timestamps need a full refresh.
Getting started
On your Snowflake data source, open Data Pipeline Settings. Enable pipeline mode, choose Incremental refresh (Recommended), and set the destination schema and retention. Save, then test one compatible experiment and inspect its queries for intermediate tables. Follow the Snowflake pipeline setup guide.
