Warehouse-native experimentation comes to BigQuery, Databricks, and Redshift
Blog post from LaunchDarkly
Warehouse-native experimentation has been expanded to support BigQuery, Databricks, and Redshift, alongside the existing Snowflake integration, allowing organizations to run experiments directly on their trusted data without duplicating it or creating alternative data versions. This approach enables seamless experiment analysis as LaunchDarkly syncs experiment exposure data into the warehouse, computes metrics directly against warehouse tables, and aggregates results for reporting. Recent upgrades have introduced advanced statistical capabilities such as sequential testing, multiple comparisons correction, the ability to add metrics post-experiment start, and result segmentation, all aimed at enhancing the confidence and precision of experiment results. These developments ensure that product and data teams can conduct faster and more accurate experiments while maintaining data governance.
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