CoinAPI to Looker Integration
AI-first ETL from CoinAPI into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads CoinAPI into Looker
Datrise syncs CoinAPI's exchange rates, symbols, trades, order book snapshots, OHLCV bars, and exchanges into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
CoinAPI: Consolidated crypto market data API.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How CoinAPI entities map to Looker
| CoinAPI entity | Looker object | Notes |
|---|---|---|
| exchange rates | coin_api_exchange_rates | date/time dimension columns events |
| symbols | coin_api_symbols | id PK · linked to coin_api_exchange_rates |
| trades | coin_api_trades | id PK · linked to coin_api_exchange_rates |
| order book snapshots | coin_api_order_book_snapshots | id PK · linked to coin_api_exchange_rates |
FAQ
How does Datrise handle CoinAPI's custom fields in Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the CoinAPI to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
More destinations for CoinAPI
Early access
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