DatriseAI-first ETL

CoinAPI to Databricks SQL Warehouse Integration

AI-first ETL from CoinAPI into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads CoinAPI into Databricks SQL Warehouse

Datrise syncs CoinAPI's exchange rates, symbols, trades, order book snapshots, OHLCV bars, and exchanges into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

CoinAPI: Consolidated crypto market data API.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How CoinAPI entities map to Databricks SQL Warehouse

CoinAPI entityDatabricks SQL Warehouse objectNotes
exchange ratescoin_api_exchange_ratesTIMESTAMP events
symbolscoin_api_symbolsid PK · linked to coin_api_exchange_rates
tradescoin_api_tradesid PK · linked to coin_api_exchange_rates
order book snapshotscoin_api_order_book_snapshotsid PK · linked to coin_api_exchange_rates

FAQ

How does Datrise handle CoinAPI's custom fields in Databricks SQL Warehouse?

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

How does the CoinAPI to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

Early access

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