DatriseAI-first ETL

Weatherstack to ClickHouse Integration

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

How Datrise loads Weatherstack into ClickHouse

Datrise syncs Weatherstack's current conditions, forecasts, historical readings, locations, temperature readings, and observation times into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.

Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.

Ideal for high-volume event analytics that need sub-second aggregation.

Endpoints

Weatherstack: Current, historical, and forecast weather lookups.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Weatherstack entities map to ClickHouse

Weatherstack entityClickHouse objectNotes
current conditionsweatherstack_current_conditionsid PK · custom fields → JSON or Map columns
forecastsweatherstack_forecastsid PK · linked to weatherstack_current_conditions
historical readingsweatherstack_historical_readingsDateTime64 events
locationsweatherstack_locationsid PK · linked to weatherstack_current_conditions

FAQ

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

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

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

It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.

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