Statsig connector
Set up the Statsig connector in Kaivo: authentication, configuration, the 21 BigQuery tables it syncs, and answers to common questions.
Written By Lauri Raivio
Last updated 16 days ago
Kaivo is a fully managed data platform that syncs your Statsig data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your data from Statsig instead of moving it.
What is the Statsig connector
Sync your Statsig experimentation data into BigQuery with Kaivo to analyse experiments, feature gates, and events.
Getting started with the Statsig connector
- Sign up for Kaivo and create a workspace.
- Connect your Statsig account.
- Choose which tables to sync.
- Wait for the initial sync to finish.
- Query your data in BigQuery or your favourite AI or BI tool.
Authenticating Statsig
Authenticate with your API Key.
Configuring the Statsig connector
When you set up the connector, you provide:
Tables and columns synced from Statsig
Kaivo syncs 21 tables from Statsig into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
audit_logs (11 columns)
audit_logs (11 columns)
Subtable: audit_logs__changes__rules__new
Subtable: audit_logs__changes__rules__new__condition_json
Subtable: audit_logs__changes__rules__new__condition_json__value
Subtable: audit_logs__changes__rules__new__rollouts
Subtable: audit_logs__changes__rules__old
Subtable: audit_logs__tags
Subtable: audit_logs__target_app_i_ds
autotunes (18 columns)
autotunes (18 columns)
Subtable: autotunes__holdout_i_ds
Subtable: autotunes__tags
Subtable: autotunes__target_apps
Subtable: autotunes__variants
dynamic_configs (17 columns)
dynamic_configs (17 columns)
Subtable: dynamic_configs__holdout_i_ds
Subtable: dynamic_configs__rules
Subtable: dynamic_configs__rules__conditions
Subtable: dynamic_configs__tags
Subtable: dynamic_configs__target_apps
dynamic_configs_rules (6 columns)
dynamic_configs_rules (6 columns)
Subtable: dynamic_configs_rules__conditions
dynamic_configs_versions (17 columns)
dynamic_configs_versions (17 columns)
Subtable: dynamic_configs_versions__holdout_i_ds
Subtable: dynamic_configs_versions__rules
Subtable: dynamic_configs_versions__rules__conditions
Subtable: dynamic_configs_versions__tags
Subtable: dynamic_configs_versions__target_apps
events (2 columns)
events (2 columns)
events_metrics (2 columns)
events_metrics (2 columns)
experiments (27 columns)
experiments (27 columns)
Subtable: experiments__groups
Subtable: experiments__health_checks
Subtable: experiments__holdout_i_ds
Subtable: experiments__primary_metric_tags
Subtable: experiments__primary_metrics
Subtable: experiments__review_settings__allowed_reviewers
Subtable: experiments__secondary_metric_tags
Subtable: experiments__secondary_metrics
Subtable: experiments__tags
Subtable: experiments__target_apps
gates (25 columns)
gates (25 columns)
Subtable: gates__holdout_i_ds
Subtable: gates__monitoring_metrics
Subtable: gates__review_settings__allowed_reviewers
Subtable: gates__rules
Subtable: gates__rules__conditions
Subtable: gates__rules__conditions__target_value
Subtable: gates__tags
Subtable: gates__target_apps
gates_rules (6 columns)
gates_rules (6 columns)
Subtable: gates_rules__conditions
Subtable: gates_rules__conditions__target_value
holdouts (15 columns)
holdouts (15 columns)
Subtable: holdouts__experiment_i_ds
Subtable: holdouts__gate_i_ds
Subtable: holdouts__layer_i_ds
Subtable: holdouts__tags
Subtable: holdouts__target_apps
ingestion_runs (2 columns)
ingestion_runs (2 columns)
ingestion_status (2 columns)
ingestion_status (2 columns)
layers (14 columns)
layers (14 columns)
Subtable: layers__holdout_i_ds
Subtable: layers__parameters
Subtable: layers__tags
Subtable: layers__target_apps
metrics (15 columns)
metrics (15 columns)
Subtable: metrics__lineage__events
Subtable: metrics__lineage__metrics
Subtable: metrics__metric_events
Subtable: metrics__metric_events__criteria
Subtable: metrics__tags
Subtable: metrics__unit_types
metrics_values (2 columns)
metrics_values (2 columns)
segments (16 columns)
segments (16 columns)
Subtable: segments__holdout_i_ds
Subtable: segments__rules
Subtable: segments__tags
Subtable: segments__target_apps
segments_ids (4 columns)
segments_ids (4 columns)
Subtable: segments_ids__ids
tags (6 columns)
tags (6 columns)
target_apps (4 columns)
target_apps (4 columns)
users (6 columns)
users (6 columns)
How the Statsig sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Statsig supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.
Frequently asked questions
How long does the initial sync take for Statsig?
It depends on how much history is in your Statsig account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.
Can I sync only some tables or columns?
Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.
What happens when Statsig's schema changes?
New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in Statsig, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.
How do I handle GDPR or data deletion requests?
Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in Statsig instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the Statsig connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Statsig data
Experiment analysis
Use experiments and events_metrics to study how experiments affect your metrics.
Feature gate inventory
Use gates and dynamic_configs to document which features run where.
Change audit
Use audit_logs to track configuration changes over time.
Use Statsig data in your AI and BI tools
Once Statsig data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with Statsig data.
Claude
Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →
Power BI
Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →
Data Studio
Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →
Tableau
The premium analytics standard, with native BigQuery integration. Setup guide →
Google Sheets
Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →
Excel
Connect via Power Query's BigQuery connector. Setup guide →
Metabase
Open-source BI tool with strong BigQuery support. Setup guide →
See our pricing page for Statsig connector pricing and plan details.
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