Looker to Neon Integration
AI-first ETL from Looker into Neon. Governed entities, incremental sync, typed landing tables.
How Datrise loads Looker into Neon
Datrise syncs Looker's looks, dashboards, explores, queries, users, and scheduled plans 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: BI platform with a modeled semantic layer.
Neon: Serverless Postgres destination with branching and autoscaling.
How Looker entities map to Neon
| Looker entity | Neon object | Notes |
|---|---|---|
| looks | looker_looks | id PK · custom fields → jsonb columns |
| dashboards | looker_dashboards | id PK · linked to looker_looks |
| explores | looker_explores | id PK · linked to looker_looks |
| queries | looker_queries | id PK · linked to looker_looks |
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
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