BigQuery is Google Cloud's warehouse, often home to your modeled analytics tables and event data. Connecting it as a data source through the Connectors library lets Alta sync tables from your dataset, where they become training data for Alta and a foundation for your metrics, dashboards, and Luna analysis. BigQuery connects with a service account.

Who this is for: Data teams on Google Cloud who want BigQuery tables measured and queried in Alta.

Before you start

Have your Project ID, the dataset you want to sync, and a service account JSON key with read access (BigQuery Data Viewer + Job User roles are typical).
Connect a source only once per workspace. If BigQuery already shows Connected, edit the existing connection.

Connect BigQuery

Open Connectors from the sidebar.
Find BigQuery via the Data tab or the Search data sources box.
Click the BigQuery card to open the Create connector screen.
Fill in the connection fields shown (Project ID, dataset, and your service account key JSON), then click Create.
Alta runs a connect test. If it fails you'll see The connect test has failed with Google'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. The BigQuery tables section lists the tables in the dataset you connected.
Use the Synced toggle to choose which tables Alta pulls in.
Turn off Show only synced tables to browse everything available.

What gets synced — your own tables

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

Fact tables — what you measure (revenue, bookings, usage/events), with an amount/quantity column and a date/timestamp column.
Dimension tables — what you slice by (customers, products, reps), carrying keys that join to your facts.
Pre-built marts — sync curated dim_ / fct_ models rather than raw tables for the cleanest results.

What you can ask this data

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]."
"What's the trend in [your metric] over the last 6 months?"
"Which [customers/products] drive the most [your metric]?"
"Compare my warehouse revenue table to CRM pipeline — forecast vs actuals."

Example use cases

One source of truth. Bring curated BigQuery marts into Alta so dashboards match what your data team publishes.
Product + revenue blend. Join event/usage tables to CRM or billing data for adoption-to-revenue views.
Self-serve analytics. Let the team ask Luna against modeled tables instead of writing SQL.

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

Paste the full JSON key. A truncated or malformed service account key is the top cause of a failed connect test.
Check the roles. The service account needs read access to the dataset and permission to run query jobs.
Watch query costs. Large, frequent syncs can run up BigQuery costs — sync only the tables you need.
Deleting is permanent. Disable instead to pause.