Snowplow to Neon Integration
AI-first ETL from Snowplow into Neon. Governed entities, incremental sync, typed landing tables.
How Datrise loads Snowplow into Neon
Datrise syncs Snowplow's events, users, sessions, contexts, page views, and event schemas 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
Snowplow: Behavioral event pipeline with a strict schema.
Neon: Serverless Postgres destination with branching and autoscaling.
How Snowplow entities map to Neon
| Snowplow entity | Neon object | Notes |
|---|---|---|
| events | snowplow_events | timestamptz 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 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 Snowplow 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 Snowplow
Early access
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