Looker → Neon
AI-first ETL from Looker into Neon. Governed entities, incremental sync, typed landing tables.
How Datrise loads Looker into Neon
Datrise syncs Looker's records, events, and configuration objects into Neon as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.
Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning by load date. Neon separates compute from storage, so Datrise batches writes to keep autoscaling compute from cold-starting on every small change.
Ideal for serverless Postgres workloads that scale to zero between syncs.
Endpoints
Looker: SaaS or API data source for analytics and warehouse sync.
Neon: Serverless Postgres destination with branching and autoscaling.
How Looker entities map to Neon
| Looker entity | Neon object | Notes |
|---|---|---|
| records | looker_records | id PK · custom fields → jsonb columns |
| events | looker_events | timestamptz events |
| configuration objects | looker_configuration_objects | id PK · linked to looker_records |
FAQ
How does Datrise handle Looker's custom fields in Neon?
Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Neon types.
How does the Looker to Neon sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE.
Related pipelines
More destinations for Looker
Early access
Connect Looker to Neon 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.