DatriseAI-first ETL

Weatherstack to PlanetScale Integration

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

How Datrise loads Weatherstack into PlanetScale

Datrise syncs Weatherstack's current conditions, forecasts, historical readings, locations, temperature readings, and observation times into PlanetScale as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Vitess sharding by tenant or entity key for very large tables. PlanetScale disallows foreign-key constraints by default, so Datrise models relationships by stable id columns rather than enforced FKs.

Ideal for horizontally scalable MySQL apps on Vitess.

Endpoints

Weatherstack: Current, historical, and forecast weather lookups.

PlanetScale: Serverless MySQL platform with safe schema workflows.

How Weatherstack entities map to PlanetScale

Weatherstack entityPlanetScale objectNotes
current conditionsweatherstack_current_conditionsid PK · custom fields → JSON columns
forecastsweatherstack_forecastsid PK · linked to weatherstack_current_conditions
historical readingsweatherstack_historical_readingsDATETIME events
locationsweatherstack_locationsid PK · linked to weatherstack_current_conditions

FAQ

How does Datrise handle Weatherstack's custom fields in PlanetScale?

Flexible values are stored as JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PlanetScale types.

How does the Weatherstack to PlanetScale sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.

Related pipelines

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