DatriseAI-first ETL

Google Analytics PlanetScale

AI-first ETL from Google Analytics into PlanetScale. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Analytics into PlanetScale

Datrise syncs Google Analytics's sessions, events, channels, conversions, and behavior cohorts into PlanetScale as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Vitess sharding by tenant or entity key for very large tables. PlanetScale disallows foreign-key constraints by default, so Datrise models relationships by stable id columns rather than enforced FKs.

Ideal for horizontally scalable MySQL apps on Vitess.

Endpoints

Google Analytics: Web and product analytics for behavior and traffic insights.

PlanetScale: Serverless MySQL platform with safe schema workflows.

How Google Analytics entities map to PlanetScale

Google Analytics entityPlanetScale objectNotes
sessionsgoogle_analytics_sessionsid PK · custom fields → JSON columns
eventsgoogle_analytics_eventsDATETIME events
channelsgoogle_analytics_channelsid PK · linked to google_analytics_sessions
conversionsgoogle_analytics_conversionsid PK · linked to google_analytics_sessions

FAQ

How does Datrise handle Google Analytics's custom fields in PlanetScale?

Flexible values are stored as JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PlanetScale types.

How does the Google Analytics to PlanetScale sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.

Related pipelines

Early access

Connect Google Analytics to PlanetScale the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.