Weatherstack to Redash Integration
AI-first ETL from Weatherstack into Redash. Governed entities, incremental sync, typed landing tables.
How Datrise loads Weatherstack into Redash
Datrise syncs Weatherstack's current conditions, forecasts, historical readings, locations, temperature readings, and observation times into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.
Ideal for lightweight, query-driven dashboards.
Endpoints
Weatherstack: Current, historical, and forecast weather lookups.
Redash: Open-source SQL client for queries, visualizations, and dashboards.
How Weatherstack entities map to Redash
| Weatherstack entity | Redash object | Notes |
|---|---|---|
| current conditions | weatherstack_current_conditions | id PK · custom fields → flattened columns for query results |
| forecasts | weatherstack_forecasts | id PK · linked to weatherstack_current_conditions |
| historical readings | weatherstack_historical_readings | temporal columns events |
| locations | weatherstack_locations | id PK · linked to weatherstack_current_conditions |
FAQ
How does Datrise handle Weatherstack's custom fields in Redash?
Flexible values are stored as flattened columns for query results, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Redash types.
How does the Weatherstack to Redash sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
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