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Frequently Asked Questions

A curated summary of the top questions asked on our Slack community, often relating to implementation, functionality, and building better products generally.
Statsig FAQs
GENERAL TROUBLESHOOTING

How to enable a metrics ingestion pipeline in Statsig

Enabling a metrics ingestion pipeline in Statsig involves a series of steps to ensure that your data is correctly integrated and utilized for analysis.

Begin by connecting your data warehouse to Statsig, which will require read access to metric and exposure data, write access for results and exposures, and the ability to run jobs and query data. Once connected, define your metric and assignment sources according to Statsig's expected schemas to map the data accurately.

Metrics can then be ingested through various mechanisms, including Server and Client SDKs, HTTP API, Integrations, Data Warehouse Ingestion, or the Data Warehouse Native Solution for enterprise contracts.

If you encounter any issues during the ingestion process, utilize the Metrics Dashboard in Statsig for debugging. After successful data import, you can proceed with feature gate or experiment implementation.

For detailed guidance on connecting your warehouse and defining sources, refer to the Statsig documentation.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
CPO
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering
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