CoinAPI to Google BigQuery Integration
AI-first ETL from CoinAPI into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads CoinAPI into Google BigQuery
Datrise syncs CoinAPI's exchange rates, symbols, trades, order book snapshots, OHLCV bars, and exchanges into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.
Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.
Ideal for Google-stack analytics and ML on serverless infrastructure.
Endpoints
CoinAPI: Consolidated crypto market data API.
Google BigQuery: Serverless analytics warehouse on GCP.
How CoinAPI entities map to Google BigQuery
| CoinAPI entity | Google BigQuery object | Notes |
|---|---|---|
| exchange rates | coin_api_exchange_rates | TIMESTAMP 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 Google BigQuery?
Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.
How does the CoinAPI to Google BigQuery sync stay up to date?
It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.
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Early access
Connect CoinAPI to Google BigQuery the easy way
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