LaunchDarkly

Warehouse Native Experimentation Build and analyze experiments in LaunchDarkly that use your trusted warehouse data to power richer analysis and insights. LaunchDarkly’s experiment analysis can use your Snowflake data to power metrics to measure and visualize the impact of your features to drive informed decisions, and allow your teams to ship new features with confidence. Configuring warehouse native Experimentation sets up three virtual warehouses in your Snowflake instance: LD_EXPORT_WH LD_EXPERIMENTATION_WH LD_SERVICE_WH Using three warehouses for periodic operations helps reduce compute and gives you visibility into where you are using compute.

The basic data flow process for LaunchDarkly’s warehouse native experimentation is as follows: End users encounter LaunchDarkly experiments and generate flag evaluations, called “assignment data” in Snowflake. LaunchDarkly SDKs send these events to LaunchDarkly. Using LaunchDarkly’s Snowflake Data Export integration , the Snowflake LD_EXPORT_WH warehouse pulls flag evaluation and experiment metadata into your Snowflake instance.

The Snowflake LD_EXPERIMENTATION_WH warehouse combines LaunchDarkly flag evaluation and experiment metadata with your Snowflake metric events data to calculate experiment results. LaunchDarkly uses the Snowflake LD_SERVICE_WH warehouse to periodically sync experiment results from Snowflake to LaunchDarkly. You can view the results in LaunchDarkly on the experiment’s Results tab.

To learn more about the steps in LaunchDarkly to create an experiment using Snowflake data, visit Creating warehouse native experiments and watch our tutorial videos . How To Get Access LaunchDarkly will review your request for access to Warehouse Native Experimentation and reach out for more information if necessary. Once approved, LaunchDarkly will share the application with you via the Snowflake Marketplace.

Then, you can install it and follow the in-app set up instructions. Learn more about the set up steps in LaunchDarkly's documentation .