Cloud data warehouses · Available
Google BigQuery for Claude, ChatGPT and Copilot
BigQuery answers in Claude, ChatGPT or Copilot come from authorized views in your reporting datasets, marketing results joined with sales, and the bytes each project scans. Row-level access policies follow each person. Before any query runs, a dry run checks how much it will scan, and the exact query behind every answer is kept. A guided pilot sets up the service account and the first datasets with your data team.
- Status
- Available
- Setup
- Guided pilot with your team
- AI assistants
- Claude, ChatGPT, Copilot Studio
- Access
- By role, view only or with approvals
Ask in a sentence
What can your team ask Google BigQuery?
Questions from marketing analyst, data analyst, finOps lead and others, answered from live Google BigQuery data and only for the people allowed to see them.
Records it works with
- Projects
- Datasets
- Tables
- Views
- Authorized views
- Row-level access policies
- Scheduled queries
- Reservations
- Marketing analyst
“From the marketing_reporting dataset, which campaigns drove the most first orders in September?”
- Data analyst
“Show weekly active accounts for the last 12 weeks from the approved product usage view.”
- FinOps lead
“Which projects scanned the most bytes last month, and which scheduled queries account for most of it?”
- Analytics engineer
“Which tables in the staging dataset have not been updated in more than three days?”
- Sales operations manager
“What is pipeline coverage for next quarter by segment, from the approved sales dataset?”
- Data steward
“Which datasets contain columns with a policy tag for personal data, and which groups can read them?”
Changes, governed
What the assistant can prepare in Google BigQuery
The assistant never saves on its own. It prepares the change, the person checks the preview and confirms, and anything above your limits waits for an approver.
- 1 Prepare The assistant drafts the change from the request.
- 2 Preview and confirm The person sees exactly what will change.
- 3 Approve Above your limits, a second person decides.
- 4 Saved and logged Written to Google BigQuery under a named person.
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Pause a scheduled query that keeps failing
The dataset owner confirms the pause; queries that feed finance reports also need the finance data lead.
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Share an authorized view with another team's dataset
The data steward approves any share that includes columns with a policy tag.
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Change the time a nightly scheduled query runs
Moving it into business hours needs the platform lead, since daytime runs compete with dashboards for slots.
Example rules. During the pilot you set the limits, the approvers and which roles may prepare each change.
Why it needs care
Governance built for Google BigQuery
BigQuery bills by data scanned or slots used, so one careless query over a raw events table can cost real money and expose more than intended. Assistants stay on authorized views. A dry run estimates the bytes first, and a query over the role's limit never starts. Each query is stored with the person who asked, ready for review.
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Sign-in by work email
Only people you invite, with a one-time code. No shared logins.
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Roles per tool
Each role reaches only the Google BigQuery records it needs.
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View only, if you prefer
Admins can keep a connection to lookups with one switch.
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A preview first
Every change is shown in full before anything is saved.
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Approval rules
Above your limits, a second person approves the change.
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One audit log
Every request tied to a named person, ready for review.
Setup
How Google BigQuery gets connected
We connect Google BigQuery for your team in a guided pilot, on the same gateway every app uses. No code on your side.
- 01
Agree the questions
We list the Google BigQuery questions and changes your team needs, and who may use each.
- 02
Connect with your admin
Your Google BigQuery administrator creates a dedicated integration user with only that access.
- 03
Test with your people
A small group tries it in Claude, ChatGPT or Copilot while we tune the tools.
- 04
Roll out with rules
Roles, approval limits and the audit log are in place before everyone joins.
Then people use it from the assistant they already have
Google BigQuery questions
Can ChatGPT query BigQuery without seeing every dataset?
Yes. The connection uses a service account that can read only the datasets and authorized views you approve. mcpbizapps then narrows that further by role, so a marketing analyst and a finance analyst can share one connection but reach different datasets.
How do you keep BigQuery costs under control?
Every query gets a dry run first. If the estimate is above the limit for that role, the assistant is told to narrow the question instead of running it. Admins can see scanned bytes by person and by project in the activity log.
Do BigQuery row-level access policies still apply?
They do. Row-level policies on tables and policy tags on columns are enforced by BigQuery itself, and mcpbizapps adds its own role rules on top. Nobody gets wider access through the assistant than the service account allows.
Does mcpbizapps keep a copy of our Google BigQuery data?
No. When someone asks a question, mcpbizapps reads the answer live from Google BigQuery through a dedicated integration user and returns it without storing it. The audit log records who asked, which tool ran and the result, with personal fields and free text removed.
Google BigQuery is a trademark of its owner. mcpbizapps is not affiliated with Google; the name describes the connection only.
Related
Often connected alongside Google BigQuery
Part of Data and analytics: Cloud warehouses, dashboards, governance and data pipelines.
Start
Bring Google BigQuery into your AI assistant
Tell us how your team uses Google BigQuery, and we will plan a guided pilot around your own questions.
- We agree the tools and approval rules with you
- Your admin creates the integration user; no code
- Business data is read live and not stored
Thank you
We received your request and will reply by email to find a time.
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