VantegrateNative API integrationIntegrate BigQuery with AI agentsand ask your warehouse questions in plain English
Metrix, Vantegrate's data agent, connects to your warehouse through the official BigQuery API: you ask in plain English, the agent turns your question into SQL (GoogleSQL), runs it as a query against your tables and gives you the result and the dashboard. Your data keeps living in your BigQuery instance.
How the data flows
What does it mean to integrate BigQuery with Vantegrate's AI agents?
Integrating BigQuery with Vantegrate's AI agents means that Metrix, the data agent, connects to your warehouse through the official BigQuery API: you ask in plain English, the agent turns your question into a SQL (GoogleSQL) query, runs it as a job against your tables and returns the answer with its dashboard. The connection uses a read-only service account.
That is different from asking an analyst for a report or exporting the data to another tool where it goes stale. Here the agent queries the current state of your warehouse (the same source of truth your data team uses) and builds the dashboard on the spot. For BI, the agent reads your data and answers questions about it; it doesn't write or load data. The information stays in your BigQuery: the agent queries it, it doesn't take it anywhere else.
What do you want to connect to your BigQuery?
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What data Metrix queries in your BigQuery
The agent works on your BigQuery datasets and tables in real time: it turns your question into SQL, runs it as a query and returns the answer, without you having to know the table names.
Datasets and tables
The agent queries your datasets, tables and views: it turns a request like "show me this quarter's top 10 customers by revenue" into a SQL query and runs it against your warehouse.
Dashboards and KPIs
On the same tables, the agent builds and refreshes KPI dashboards and answers multi-part questions without you defining fields or filters by hand.
Insights and trends
You ask for statistics or trends in your data and the agent spots what matters in the dataset, with an explanation in plain English and a visualization.
How the integration works, step by step
Vantegrate handles the setup and rollout. Your team authorizes, validates and starts using it.
Connection through the REST API
We connect to the official BigQuery API with a service account (server-to-server credentials that sign JWTs, or Application Default Credentials). No scrapers and no fragile bridges.
Permissions and datasets
We define the IAM roles and which datasets and tables the agent can access, with the minimum read-only permissions the BI use case needs.
From plain English to SQL
The agent turns your question into a GoogleSQL query, runs it as a job scoped by columns and partitions, and returns the answer with its dashboard.
Validation with real data
We test against your warehouse and your real tables, validate the answers with your data and IT teams, and roll out in phases.
Querying BigQuery live vs. exporting to another tool
BigQuery is queried with SQL (GoogleSQL), which follows the ANSI standard, and every query runs as a job through the REST API; storage and compute scale separately, so you don't provision servers. Metrix works on top of that API: it turns your natural-language question into the SQL query and runs it, without you knowing the table names or writing a line of code.
The difference from exporting the data to another BI tool shows up in every question. The agent reads the current state of the warehouse at that moment, not a copy that drifts with every load. BigQuery already supports natural-language-to-SQL translation natively, so the platform itself has validated the pattern; Metrix is the conversational and dashboard layer on top of your account.
| Dimension | Exporting to another tool | Metrix on the BigQuery API |
|---|---|---|
| Connection | A copy or batch sync | BigQuery REST API, live |
| Authentication | Broad credentials or a manual export | Service account with scoped IAM |
| Reading data | A copy that drifts | Current state of the warehouse, at that moment |
| How you ask | You need to know the tables or wait for the analyst | In plain English; the agent translates it into SQL |
| Permissions | Broad access | Read-only, on the minimum datasets you define |
Comparison of integration patterns; the exact scope is defined with your team based on your datasets and your Google Cloud project.
Your data stays in your warehouse: the agent queries your BigQuery through the API; it doesn't take your database anywhere else. The connection uses a read-only service account with minimum permissions, and every query is logged and auditable in your account's job history.
How the agent keeps the cost of each query in check
BigQuery on-demand pricing charges for the bytes processed in the columns a query reads, not per row or per number of questions. That's why a SELECT * with no filters can scan (and bill) entire terabytes even if the query has a LIMIT, because LIMIT doesn't reduce the bytes scanned. For an agent that generates SQL, that's a real caveat to manage.
Metrix builds its queries by restricting the columns and relying on your tables' partitions and clusters, which are the levers that reduce the bytes scanned. Materialized views, which serve cached results, help when the same query runs often. The cost ends up depending on which columns are queried, not on how many questions your team asks.
About the cost model: BigQuery's on-demand price is $6.25 per TiB processed, with the first 1 TiB per month free, according to Google Cloud's pricing page, and it varies by region. The integration is designed to scan as little as possible: scoped columns, partition filters and reuse of materialized views.
You've seen how it connects. Want to walk through your case?
Tell us how your BigQuery is set up and we'll tell you what data we need and where the agent connects.
An agent connected to your BigQuery vs. a standalone tool
| Standalone AI tool | Agent connected to your BigQuery | |
|---|---|---|
| Data source | A copy that drifts | Your source of truth, live |
| Where your data lives | In a third-party system | In your environment, encrypted end to end |
| Result of each question | A one-off export | An answer from live data, where your team works |
| Permissions and audit | Broad and hard to audit | Read-only role and a log of every query |
| Reaching production | Often stalls as a pilot | Validated, phased rollout |
Why add a conversational layer on top of your warehouse
Third-party figures, each with its published source, to size up the context. None of them is a Vantegrate result.
1 TiB/month
Free on-demand query volume in BigQuery each month, before $6.25 per additional TiB
95%
Of generative AI pilots never reach production with measurable business impact
BigQuery's price is published by Google Cloud, not by Vantegrate, and it varies by region and by pricing model (capacity vs. on-demand). The MIT NANDA figure reflects the gap between experimenting with AI agents and running them in production.
Which agent runs on your BigQuery
The BigQuery use case is conversational BI: the data agent queries your warehouse in real time and builds the dashboards, with your data always in your instance.
The integration doesn't take your data anywhere
The principle is simple: AI comes to your data, not your data to the AI. The agent queries your BigQuery through the official API, with a read-only service account, and runs on certified cloud infrastructure.
- Connection through the BigQuery REST API with a service account (JWT or Application Default Credentials), with no scrapers and no fragile bridges.
- The agent queries your datasets and tables to answer; for BI it doesn't write or load data, and your database stays in your BigQuery instance.
- Access scoped by IAM with minimum read-only permissions, and every query logged in the job history.
- The agents run on Salesforce or Oracle Cloud Infrastructure, whose SOC 2 and ISO 27001 certifications belong to those platforms, not to Vantegrate or BigQuery.
Frequently asked questions about the BigQuery integration
What data and IT teams usually ask before connecting an AI agent to their warehouse.
Do I need to know SQL to query BigQuery with Metrix?
Do I need to know SQL to query BigQuery with Metrix?
No. Metrix turns your plain-English question into the SQL (GoogleSQL) query and runs it for you against your warehouse, without you knowing the table names or writing code. BigQuery already supports natural-language-to-SQL translation natively, so the platform itself has validated the pattern; Metrix is the conversational and dashboard layer on top of your account.
How does Metrix connect to BigQuery securely?
How does Metrix connect to BigQuery securely?
Through the BigQuery REST API with a read-only service account (server-to-server credentials that sign JWTs, or Application Default Credentials); it also supports OAuth 2.0 for apps with an end user. Access is scoped by IAM roles and minimum datasets, your keys are never exposed to the end user, and every query is logged in your account's job history.
How much does it cost for the agent to query BigQuery?
How much does it cost for the agent to query BigQuery?
BigQuery's on-demand model charges for the bytes processed in the columns the query reads, not per row or per number of questions: $6.25 per TiB according to Google Cloud's pricing page, which varies by region, with the first 1 TiB per month free. The agent builds queries that restrict columns and use partitions, clusters and materialized views to scan as little as possible and keep the cost low.
Does Metrix modify the data in my BigQuery?
Does Metrix modify the data in my BigQuery?
Not for the BI use case. The agent reads your warehouse and answers questions about it (queries and dashboards); it doesn't write or load data, and it connects with a read-only service account. The information stays in your BigQuery instance, the agent queries it with minimum permissions, and every job is logged. The principle is that AI goes to your data, not your data to the AI.
Metrix, the data agent
How the agent turns your question into SQL, queries your warehouse and builds the dashboard in plain English.
Learn moreAll integrations
The full map: the warehouses, ERPs, CRMs and tools the AI agents connect to.
Learn moreAI agents for enterprises
The map of the Vantegrate suite: sales, marketing, documents, data and logistics.
Learn moreLet's connect AI agents to your BigQuery
Tell us how your warehouse is set up and we'll show you what the agent queries, which service account and permissions we request, and what querying your data in plain English looks like. A 30-minute conversation, no commitment.
Francisco Morales, co-founder, takes your call. We reply on WhatsApp within 4 business hours, no strings attached.
