DatriseAI-first ETL

Weatherstack to DuckDB Integration

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

How Datrise loads Weatherstack into DuckDB

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

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Weatherstack: Current, historical, and forecast weather lookups.

DuckDB: In-process analytics database for fast local OLAP.

How Weatherstack entities map to DuckDB

Weatherstack entityDuckDB objectNotes
current conditionsweatherstack_current_conditionsid PK · custom fields → JSON or STRUCT columns
forecastsweatherstack_forecastsid PK · linked to weatherstack_current_conditions
historical readingsweatherstack_historical_readingsTIMESTAMP WITH TIME ZONE events
locationsweatherstack_locationsid PK · linked to weatherstack_current_conditions

FAQ

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

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

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

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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