DatriseAI-first ETL

Weatherstack to Neon Integration

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

How Datrise loads Weatherstack into Neon

Datrise syncs Weatherstack's current conditions, forecasts, historical readings, locations, temperature readings, and observation times 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

Weatherstack: Current, historical, and forecast weather lookups.

Neon: Serverless Postgres destination with branching and autoscaling.

How Weatherstack entities map to Neon

Weatherstack entityNeon objectNotes
current conditionsweatherstack_current_conditionsid PK · custom fields → jsonb columns
forecastsweatherstack_forecastsid PK · linked to weatherstack_current_conditions
historical readingsweatherstack_historical_readingstimestamptz events
locationsweatherstack_locationsid PK · linked to weatherstack_current_conditions

FAQ

How does Datrise handle Weatherstack'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 Weatherstack 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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