DatriseAI-first ETL

Partnerstack Neon

AI-first ETL from Partnerstack into Neon. Governed entities, incremental sync, typed landing tables.

How Datrise loads Partnerstack into Neon

Datrise syncs Partnerstack'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

Partnerstack: SaaS or API data source for analytics and warehouse sync.

Neon: Serverless Postgres destination with branching and autoscaling.

How Partnerstack entities map to Neon

Partnerstack entityNeon objectNotes
recordspartnerstack_recordsid PK · custom fields → jsonb columns
eventspartnerstack_eventstimestamptz events
configuration objectspartnerstack_configuration_objectsid PK · linked to partnerstack_records

FAQ

How does Datrise handle Partnerstack'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 Partnerstack 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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