CoinGecko to DuckDB Integration
AI-first ETL from CoinGecko into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads CoinGecko into DuckDB
Datrise syncs CoinGecko's coins, market prices, market caps, trading volumes, exchanges, and price history into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.
Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.
Ideal for local and notebook analytics without standing up a server.
Endpoints
CoinGecko: Cryptocurrency prices and market data.
DuckDB: In-process analytics database for fast local OLAP.
How CoinGecko entities map to DuckDB
| CoinGecko entity | DuckDB object | Notes |
|---|---|---|
| coins | coingecko_coins_coins | id PK · custom fields → JSON or STRUCT columns |
| market prices | coingecko_coins_market_prices | id PK · linked to coingecko_coins_coins |
| market caps | coingecko_coins_market_caps | id PK · linked to coingecko_coins_coins |
| trading volumes | coingecko_coins_trading_volumes | id PK · linked to coingecko_coins_coins |
FAQ
How does Datrise handle CoinGecko's custom fields in DuckDB?
Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.
How does the CoinGecko to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
Related pipelines
More destinations for CoinGecko
Early access
Connect CoinGecko to DuckDB the easy way
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