CoinGecko to Mode Integration

How Datrise loads CoinGecko into Mode

Datrise syncs CoinGecko's coins, market prices, market caps, trading volumes, exchanges, and price history into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

CoinGecko: Cryptocurrency prices and market data.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How CoinGecko entities map to Mode

CoinGecko entity	Mode object	Notes
coins	`coingecko_coins_coins`	id PK · custom fields → flattened columns for SQL and notebooks
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 Mode?

Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.

How does the CoinGecko to Mode sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the queried tables.

Related pipelines

Early access

Connect CoinGecko to Mode the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.