BigQuery Integration

Google BigQuery is a serverless data warehouse for analyzing large datasets with SQL. Connect BigQuery to Basedash to ask questions, build dashboards, and automate reporting with AI.

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Chat/BigQuery growth

Connected

Show BigQuery weekly trend over the last 12 weeks, with the prior period as a comparison.

Pulled the last 12 weeks from bigquery. Up +38% vs. the prior period — growth accelerating in the last four weeks.

BigQuery weekly

94+38%(42)

BigQuery

68+13%(38)

Prior period

12 weeks

Save it to the Acme Corp dashboard.

Ask a follow-up about BigQuery…↵

Basedash connects to Google BigQuery with a service account and queries your datasets directly, importing the table and column descriptions you already maintain.

Read the BigQuery setup guide

Create a service account
In Google Cloud, create a dedicated service account with BigQuery roles and download its JSON key.
Upload the key
Select BigQuery on the Data page and upload the JSON key. Basedash reads the project from it.
Connect and sync datasets
Basedash checks access, then syncs dataset schemas along with their existing descriptions.

IAM roles for the service account

roles/bigquery.dataViewer to read data
roles/bigquery.jobUser to run queries
roles/bigquery.resourceViewer to list projects and datasets

Imagine ChatGPT or Claude, but with full context of your BigQuery schema and how your team uses it. Basedash answers in plain language, backed by real queries.

Try asking

Revenue by region from BigQuery for the last four quarters.
Which BigQuery tables power our core KPIs, and how fresh are they?
Compare weekly active accounts in BigQuery against last year.
Break down BigQuery orders by product line and channel.

Plain-language queries

Ask follow-ups in natural language. Basedash translates them into SQL against your BigQuery data.

Knows your business

Basedash learns your KPIs, schemas, and the way your team talks about BigQuery.

Agentic by default

Runs multiple queries, resolves errors, and keeps iterating until the answer is right.

Keep querying BigQuery directly, and sync your SaaS tools into a managed warehouse to see how sales, finance, product, and operations actually connect.

Queries run in placeBasedash connects to BigQuery directly and queries your data where it lives. No copies to keep in sync.
Scoped credentialsConnect with a dedicated BigQuery user or key so Basedash only sees the data you grant.
SaaS data alongsideSync CRM, billing, and support tools into Basedash Warehouse and analyze them in the same workspace.

Explore the warehouse

Common questions about BigQuery and Basedash

Which IAM roles does Basedash need in BigQuery?

Three: roles/bigquery.dataViewer to read data, roles/bigquery.jobUser to run query jobs, and roles/bigquery.resourceViewer to list projects and datasets. Put them on a dedicated service account, upload its JSON key in Basedash, and rotate the key regularly.

Does Basedash use my existing BigQuery table and column descriptions?

Yes. Basedash automatically imports the table and column descriptions already set in BigQuery, so the AI starts with that context without extra setup. You can add more context in Basedash for transformed data, business-specific metrics, or names that don't explain themselves.

How do I keep BigQuery costs predictable with Basedash?

Partition and cluster large tables so queries scan less data, materialize commonly used views, and set query cost limits in Google Cloud. Monitor costs and quotas there, and check the BigQuery audit logs if a query fails.