Snowflake

Cloud Delivery is in limited availability. To request access, reach out to your sales or account manager.

Overview

Coin Metrics exposes Cloud Delivery datasets as tables in Snowflake through Secure Data Sharing. You mount a read-only database in your own Snowflake account and query it with SQL. No data is copied into your account and you are not charged Snowflake storage for it. You pay only for the compute you use to run queries.

For which datasets exist and how far back they go, see Cloud Delivery.

Get access

What to provide

Send your account manager your Snowflake organization name and account name. You can find both by running the following in your account.

SELECT CURRENT_ORGANIZATION_NAME(), CURRENT_ACCOUNT_NAME();

Coin Metrics adds your account to a share. Shares are curated per customer, so you see the tables covered by your entitlements rather than every table published.

Mount the share

Once you have been added, confirm the share is visible.

SHOW SHARES;

Create a database from it. You choose the database name, so pick whatever fits your naming conventions.

CREATE DATABASE coin_metrics
  FROM SHARE <provider_account>.<share_name>;

Grant your reader roles access to it.

GRANT IMPORTED PRIVILEGES ON DATABASE coin_metrics TO ROLE analyst;

List what the share exposes, then set its schema as your session default so you can refer to tables by name alone. Your account manager can confirm the schema name if the listing is not clear.

SHOW SCHEMAS IN DATABASE coin_metrics;
SHOW TABLES IN DATABASE coin_metrics;

USE SCHEMA coin_metrics.<schema>;

Verify the mount worked.

SELECT COUNT(*) FROM market_trades WHERE dt = '2026-07-28';

Shares are region-bound. A direct share works when your account is in the same cloud region as the provider account. If yours is elsewhere, your account manager will arrange a listing that handles cross-region fulfillment.

Marketplace listings

Coin Metrics also publishes Cloud Delivery datasets as private listings. A listing is the better route when you need cross-region or cross-cloud delivery, or when you prefer to discover and request the data through the Snowflake interface rather than by exchanging account identifiers. Ask your account manager which route suits you.

Understand the layout

Ten tables are published. A curated share may expose fewer, depending on your entitlements.

Table	Contents
market_trades	Executed trades
market_candles	OHLCV bars
market_quotes	Best bid and ask
market_orderbooks_snapshots	Point-in-time order book state
market_orderbooks_updates	Incremental order book changes
reference_rates	Coin Metrics reference rates
reference_data_markets	Market catalog
reference_data_assets	Asset catalog
reference_data_exchanges	Exchange catalog
reference_data_pairs	Pair catalog

Partition columns

Every market dataset carries partition columns in addition to its data columns. These are derived from the underlying file layout rather than stored in the file body, and filtering on them is what keeps queries fast.

Table	Partition columns
market_trades	market_type, exchange, dt
market_candles	market_type, exchange, frequency, dt
market_quotes	market_type, exchange, granularity, dt
market_orderbooks_snapshots	market_type, exchange, depth, granularity, dt
market_orderbooks_updates	market_type, exchange, depth, granularity, dt
reference_rates	frequency, dt
Reference data tables	None

dt is a DATE holding the UTC date of the data. market_type is one of spot, future, or option. exchange is the normalized exchange identifier, lowercase with non-alphanumeric characters replaced by underscores, so binance.us appears as binance_us.

Data columns

Column names and meanings match the API fields documented on the dataset pages linked from Cloud Delivery, except for the omissions listed under Schema differences. Two further differences are worth knowing.

Types are native, not strings. The API returns prices and amounts as JSON strings to preserve precision. Here they are FLOAT, and timestamps are TIMESTAMP_NTZ in UTC rather than ISO-8601 strings. If you need exact decimal arithmetic, cast to NUMBER with an explicit scale.

Order book sides are semi-structured. In market_orderbooks_snapshots and market_orderbooks_updates, asks and bids are VARIANT columns holding arrays of price and size objects. Use LATERAL FLATTEN to expand them, as shown below. The updates table additionally carries a type column that the snapshots table does not.

Read the data

The examples below use unqualified table names, so run the USE SCHEMA statement from Mount the share first, or qualify each name with your database and schema.

Trades for one market and day

SELECT market, time, price, amount, side
FROM market_trades
WHERE market_type = 'spot'
  AND exchange = 'coinbase'
  AND dt = '2026-07-28'
  AND market = 'coinbase-btc-usd-spot'
ORDER BY time
LIMIT 100;

Daily candles over a date range

SELECT dt, market, price_open, price_high, price_low, price_close, volume
FROM market_candles
WHERE market_type = 'spot'
  AND exchange = 'binance'
  AND frequency = '1d'
  AND dt BETWEEN '2026-01-01' AND '2026-06-30'
  AND market = 'binance-btc-usdt-spot'
ORDER BY dt;

Reference rates

Metric columns are named after the metric, for example ReferenceRateUSD. Snowflake identifiers are case-insensitive unless quoted, so you can write them in any case.

SELECT asset, time, ReferenceRateUSD
FROM reference_rates
WHERE frequency = '1h'
  AND dt = '2026-07-28'
  AND asset IN ('btc', 'eth')
ORDER BY asset, time;

Flattening an order book

Expanding the asks array gives one row per price level.

SELECT
  ob.market,
  ob.time,
  level.index                     AS level_index,
  level.value:price::FLOAT        AS price,
  level.value:size::FLOAT         AS size
FROM market_orderbooks_snapshots AS ob,
     LATERAL FLATTEN(input => ob.asks) AS level
WHERE ob.market_type = 'spot'
  AND ob.exchange = 'coinbase'
  AND ob.dt = '2026-07-28'
  AND ob.market = 'coinbase-btc-usd-spot'
  AND ob.time = '2026-07-28 00:00:00'
ORDER BY level_index
LIMIT 20;

Joining to reference data

SELECT t.market, m.base, m.quote, m.type, COUNT(*) AS trade_count
FROM market_trades AS t
JOIN reference_data_markets AS m
  ON t.market = m.market
WHERE t.market_type = 'spot'
  AND t.exchange = 'coinbase'
  AND t.dt = '2026-07-28'
GROUP BY t.market, m.base, m.quote, m.type
ORDER BY trade_count DESC;

Querying efficiently

The shared tables read Parquet from object storage at query time rather than from native Snowflake storage, so pruning matters more than it would on a table in your own account.

• Always filter on dt. It is the highest-leverage filter available. An unfiltered query scans every day of history.
• Filter on exchange and market_type too. Each one eliminates a large share of the files before any are opened.
• Filter on frequency for candles. Eight frequencies share the table, so omitting it reads roughly eight times more data than you need.
• Compare dt to date literals, not expressions. Wrapping the column in a function can prevent partition elimination.
• Materialize hot queries. If you run the same aggregation repeatedly, create a native table or materialized view from the result rather than rescanning the shared table each time.

Limits and notes

• The tables are read-only. They do not support INSERT, UPDATE, DELETE, or MERGE. To modify data, copy it into your own table first with CREATE TABLE ... AS SELECT.
• Queries are slower than on native tables. Every query reads Parquet from object storage. Pruning is at file level rather than micro-partition level, and there is no automatic clustering. For repeated heavy analytics, copy the slice you need into a native table.
• You pay for compute. Snowflake storage is not charged for shared data, but every query runs on your warehouse and is billed to you.
• No Time Travel on shared data. If you need historical snapshots of the data as it stood, materialize your own copies.
• Reference data has no history. The four catalog tables reflect the current state and are overwritten daily. They have no dt column.
• Shares cannot be re-shared. Snowflake does not permit a consumer to re-share a database created from a share. If another account in your organization needs access, ask your account manager to add it to the share.

Related

• Cloud Delivery: datasets, coverage, and the update schedule.
• Amazon S3: the same data as files in object storage.
• Snowflake documentation on consuming shares