Warehouse-Native Analytics

Warehouse-native analytics lets you run experiment analysis directly against your existing data warehouse (Snowflake, BigQuery, or Redshift) instead of relying solely on events tracked through the platform's ingestion pipeline. This is useful when your source-of-truth metrics already live in the warehouse, or when you need to analyze large datasets that are impractical to re-ingest.

Supported Warehouses

Setup Overview

1. Create a warehouse connection (credentials stored encrypted)
2. Test the connection
3. Trigger a sync for a specific experiment to pull results from the warehouse
4. View results through the standard results API

API Reference

POST /api/v1/warehouse/connections/test

Test warehouse credentials without persisting them. Always run this before creating a connection.

curl -X POST "http://localhost:8000/api/v1/warehouse/connections/test" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Production Snowflake",
    "warehouse_type": "snowflake",
    "host": "myaccount.snowflakecomputing.com",
    "database": "ANALYTICS",
    "username": "EXP_PLATFORM_SVC",
    "password": "••••••••"
  }'

Response

{
  "success": true,
  "latency_ms": 142,
  "error": null
}

GET /api/v1/warehouse/connections

List all active warehouse connections. Passwords and credentials are never returned.

POST /api/v1/warehouse/connections

Create a new warehouse connection. Requires DEVELOPER or ADMIN role.

Snowflake Example

{
  "name": "Production Snowflake",
  "warehouse_type": "snowflake",
  "host": "myaccount.snowflakecomputing.com",
  "database": "ANALYTICS",
  "username": "EXP_PLATFORM_SVC",
  "password": "service_account_password"
}

BigQuery Example

{
  "name": "BigQuery Analytics",
  "warehouse_type": "bigquery",
  "project_id": "my-gcp-project",
  "database": "analytics_dataset",
  "username": "service-account@project.iam.gserviceaccount.com"
}

Redshift Example

{
  "name": "Production Redshift",
  "warehouse_type": "redshift",
  "host": "my-cluster.abc123.us-east-1.redshift.amazonaws.com",
  "database": "analytics",
  "username": "exp_platform"
}

GET /api/v1/warehouse/connections/{connection_id}

Retrieve a specific connection (no credentials in response).

DELETE /api/v1/warehouse/connections/{connection_id}

Soft-delete a connection. Requires DEVELOPER or ADMIN role.

POST /api/v1/warehouse/sync/{experiment_id}

Trigger a warehouse sync for a specific experiment. The sync pulls assignment and metric data from the warehouse and updates the platform's results.

curl -X POST "http://localhost:8000/api/v1/warehouse/sync/exp-uuid" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "connection_id": "conn-uuid",
    "assignments_table": "analytics.exp_assignments",
    "events_table": "analytics.exp_events",
    "date_range_start": "2026-02-01",
    "date_range_end": "2026-02-28"
  }'

Response

{
  "sync_id": "sync-uuid",
  "experiment_id": "exp-uuid",
  "status": "running",
  "rows_processed": 0,
  "started_at": "2026-03-02T14:00:00Z"
}

Poll GET /api/v1/warehouse/sync/{sync_id} for status updates.

SQL Safety

All SQL generated by the warehouse sync engine is parameterized and validated before execution. Table and column names are allowlisted against a schema validation step to prevent SQL injection. User-supplied query fragments are not accepted.

Expected Table Schemas

Assignments Table

Events Table

Permissions