Snowflake and BigQuery. Same app.

Who each is for

Snowflake tends to show up where a team wants a dedicated compute layer they can size up and down independently of storage, and where the SQL is close enough to standard ANSI that migrating from Postgres or Redshift felt low-risk. BigQuery tends to show up wherever the rest of the stack is already Google Cloud, or where the team wants to skip managing warehouse sizing entirely and pay per query instead.

Plenty of teams end up running both: Snowflake for a core warehouse built years ago, BigQuery for a newer project or a client engagement that standardized on Google Cloud. That's the case this page is really for.

Side by side

A query that looks almost the same, and isn't

The same daily-signups question, written for each:

-- Snowflake
SELECT plan_name, COUNT(*) AS signups
FROM analytics.public.subscriptions
WHERE created_at >= DATEADD(day, -1, CURRENT_DATE())
GROUP BY plan_name;

-- BigQuery
SELECT plan_name, COUNT(*) AS signups
FROM `analytics_prod.subscriptions`
WHERE created_at >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY)
GROUP BY plan_name;

Same shape, different date functions, different table-name quoting. QueryFlow's editor and the Ask panel both track which dialect a given connection speaks, so autocomplete and anything Claude writes matches the warehouse you're actually pointed at, not a generic SQL guess.

The cost question people actually ask

Snowflake cost is mostly a function of how long a warehouse runs and how large you sized it, so an idle warehouse left running is where money leaks. BigQuery cost is mostly a function of bytes scanned per query, so an unfiltered SELECT * against a huge table is where money leaks. Neither problem is something QueryFlow solves for you. BigQuery's own query validator will estimate bytes scanned before you run something large; there's no equivalent inside QueryFlow, so check there first on anything against a table you don't already know the size of.

Browsing the schema, in either one

The Explorer tab follows each warehouse's own structure rather than forcing a single generic tree onto both. A Snowflake connection lists databases, then schemas, then tables. A BigQuery connection lists projects, then datasets, then tables. The right-click actions once you're looking at a table, generate a SELECT with a LIMIT, insert the name, or copy it, work identically either way, which is the part that actually saves time day to day: you don't have to remember a different set of shortcuts depending on which warehouse tab you're in.

Migrating from one to the other

If a project is moving off Snowflake onto BigQuery, or the reverse, QueryFlow doesn't automate the migration itself, connections and pipelines on the new warehouse still need to be built. What it does help with is running both side by side during the transition: query the old warehouse in one tab to check a number, query the new one in another to confirm it matches, without two separate applications open. See the Snowflake to BigQuery migration guide if that's the direction you're headed.

When to pick each

Pick Snowflake when you want predictable, sizeable compute you control directly, or when your data is already there. Pick BigQuery when you'd rather not think about warehouse sizing, or your stack is already Google Cloud. Pick both, in the same QueryFlow window, when different projects or clients already made that decision for you before you had a say.