Snowflake is often the single source of truth where teams model their cleanest, most trusted data. Connecting it as a data source through the Connectors library lets Alta sync the tables you choose straight from your warehouse, where they become training data for Alta and a foundation for your metrics, dashboards, and Luna analysis. Snowflake connects with database credentials.

Who this is for: Data and analytics teams who already model data in Snowflake and want it measured and queried in Alta.

Before you start

Have your Snowflake connection details ready: account/host, warehouse, database, schema, role, and a username and password for a user with read access to the tables you want.
Create a dedicated read-only role/user for Alta (USAGE on the warehouse, SELECT on the schema) so access is easy to audit and revoke.
If your account uses network policies, allow access from Alta.
Connect a source only once per workspace. If Snowflake already shows Connected, edit the existing connection.

Connect Snowflake

Open Connectors from the sidebar.
Find Snowflake via the Data tab or the Search data sources box.
Click the Snowflake card to open the Create connector screen.
Fill in the connection fields shown (host, warehouse, database, schema, role, username, password), then click Create.
Alta runs a connect test. If it fails you'll see The connect test has failed with Snowflake's error — fix the field and retry.
The card then shows Connected and Data is syncing until the first sync finishes.

Choose which tables sync

Open the connection. In the Snowflake tables section you'll see the tables available in the database/schema you connected.
Use the Synced toggle to choose exactly which tables Alta pulls in.
Turn off Show only synced tables to browse everything available.

What gets synced — your own tables

Unlike app connectors, a warehouse has no fixed schema — you decide which of your own tables sync, and the column names are whatever you've modeled. A good rule of thumb:

Fact tables — the things you measure: a revenue, bookings, usage, or subscriptions table. Look for an amount/quantity column and a date/timestamp column to trend on.
Dimension tables — the things you slice by: customers/accounts, products, reps, regions. These carry a key that joins back to your fact tables.
Pre-built marts — if you already have curated dim_ / fct_ models, sync those rather than raw tables for the cleanest results.

What you can ask this data

Because the schema is yours, the questions follow your tables. Once synced, build metrics and dashboards or ask Luna / Ask AI, for example:

"Sum [your amount column] by month from [your fact table]." — a time trend of your core metric.
"What's the trend in [your metric] over the last 6 months?"
"Which [customers/products/regions] drive the most [your metric]?" — fact joined to a dimension.
"Compare my warehouse revenue table to CRM pipeline — forecast vs actuals." — blends Snowflake with a synced CRM.

Example use cases

Single source of truth in Alta. Sync your curated revenue/usage marts so dashboards and Luna use the same numbers your data team trusts.
Blend warehouse + app data. Join modeled warehouse metrics to CRM pipeline or billing data for forecast-vs-actuals and net-revenue views.
Self-serve questions for the team. Once your marts are in, non-analysts can ask Luna instead of filing SQL requests.

Keep it in sync

Sync status shows Last sync (Succeeded/Failed) and the Sync frequency.
Click Sync now to refresh immediately; it's disabled while a sync runs.
Use the overflow menu (⋯) to Disable, Enable, or Delete.

Tips and common pitfalls

Whitelist Alta's access if needed. Network policies that block external access are a common reason the connect test fails.
Use a read-only role with the right warehouse. The role needs USAGE on the warehouse and SELECT on the tables.
Sync curated tables, not everything — warehouses can have thousands of tables; pick the marts that matter.
Deleting is permanent. Disable instead to pause.