Snowplow to Google BigQuery Integration
AI-first ETL from Snowplow into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Google BigQuery
Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.
Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.
Ideal for Google-stack analytics and ML on serverless infrastructure.
Endpoints
Snowplow: Behavioral event pipeline with a strict schema.
Google BigQuery: Serverless analytics warehouse on GCP.
How Snowplow entities map to Google BigQuery
| Snowplow entity | Google BigQuery object | Notes |
|---|---|---|
| events | snowplow_events | TIMESTAMP events |
| users | snowplow_users | id PK · linked to snowplow_events |
| sessions | snowplow_sessions | id PK · linked to snowplow_events |
| contexts | snowplow_contexts | id PK · linked to snowplow_events |
FAQ
How does Datrise handle Snowplow's custom fields in Google BigQuery?
Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.
How does the Snowplow to Google BigQuery sync stay up to date?
It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.
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Early access
Connect Snowplow to Google BigQuery the easy way
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