How to connect Google BigQuery to an AI data analyst
Updated 2026-07-01
An AI data analyst is most useful when it runs on your real data. This guide shows how to connect Google BigQuery so you can ask questions in plain English and get charts, dashboards and reports back — without writing SQL by hand.
The setup takes a few minutes: connect with a read-only user, let the analyst read the schema, and ask your first question. Your credentials are encrypted, and the analysis runs on your own AI provider key.
- 1
Get your Google BigQuery connection details
BigQuery authenticates with a service account, not a password. In Google Cloud, create a service account, download its JSON key, and note your project id and dataset.
- 2
Create a read-only user (recommended)
Give the service account the BigQuery Data Viewer and Job User roles on the project (or just the dataset). Data Viewer is read-only, so it can query but never modify.
- 3
Create your account and add your AI key
Sign up, then add your own AI provider key (bring-your-own-key). If you don't have one yet, a free key takes about a minute to create — your key is encrypted and used only for your requests.
- 4
Add the Google BigQuery connection
Add a data source, choose BigQuery, and provide your project id and the service-account JSON key. Test and save — the key is encrypted at rest.
- 5
Let the AI read and annotate your schema
The analyst reads your datasets and tables and annotates them. With dbt on BigQuery, point it at your target dataset so it uses your modelled tables.
- 6
Ask your first question in plain English
Ask something like "Event counts by day for the last month". On complex questions the analyst shows a readable query plan before it runs anything; review it, then get a chart plus the data behind it. Refine by chatting, then pin it to a dashboard or save it as a report.
Why connect Google BigQuery instead of exporting
A static export goes stale the moment you download it, and a general AI assistant that only sees a pasted file guesses what your columns mean. A connected AI data analyst reads live Google BigQuery data, shows the query plan before running it, and remembers what your fields mean between sessions — so answers stay current and consistent.
Google BigQuery: things to know
- Use a service account with Data Viewer only — avoid keys that can write or delete.
- BigQuery bills by bytes scanned, so ask for specific date ranges to keep queries cheap.
Example questions to ask your Google BigQuery data
- Event counts by day for the last month
- Revenue by country this year
- Retention by signup cohort
Keeping it safe
- Connect with a read-only user so the analyst can never change your data.
- A read-only SQL guard blocks anything that isn't a read query.
- Connection details and your AI key are encrypted at rest.
- Your business data isn't stored or used to train any model.
Frequently asked questions
Related
Get your end-to-end AI business intelligence now.
Conversational analytics with AI that understands your data — no SQL, no data team required.