Logo Google BigQuery

Gudang awan

Connect Google BigQuery to Excel, Sheets and AI

Google's serverless data warehouse. The agent authenticates and runs SELECT — results stream to Excel, Sheets, Nova or MCP.

Start free 11 destinations
1connection
0inbound ports
hanya bacaenforced

One connection, every surface

Where your BigQuery data can go

Connect BigQuery once and the same read-only connection feeds all of these — no second setup, no second copy of the data. 9 of 11 have a step-by-step guide.

Didukung

BigQuery to Power BI

Power Query M

Paste the generated Power Query M into the Power BI Advanced Editor and the report reads live BigQuery results over HTTPS — no ODBC driver, no database port opened.

How Power BI works no BigQuery walkthrough written yet
Didukung

BigQuery alerts and reports

Slack · Discord · Email · Webhook

Put a BigQuery query on a schedule and have the rows delivered to Slack, Discord, email or a signed webhook — or hold the message until a row count, threshold or percentage change crosses the line you set.

How alerts and reports work no BigQuery walkthrough written yet

Cara kerjanya

5 steps, no inbound firewall change

01

Install the Network Agent anywhere with outbound HTTPS. BigQuery is a REST API, so there is no port and no listener involved.

02

Give it a project and, usually, a default dataset. Authenticate with a service account key — pasted in, or left as a file on the agent's disk and referenced by path.

03

Grant that service account only what it should read. Everything Query Streams can see is what the service account can see.

04

Pick the datasets worth exposing. The agent reads their tables and columns, flattening nested records so you can see what is actually there.

05

Write read-only SQL, or let Nova write it. Read the saved query from Excel, Sheets, Power BI, MCP or REST.

Feature deep-dive

What BigQuery gives you

Nested data you can actually see

BigQuery's strength is also what makes it awkward to browse: records nest, arrays repeat, and half the tools that connect to it show you a column called payload and nothing more.

  • Nested records are flattened in the catalog as parent.child, so the Query Builder shows the fields inside a STRUCT rather than the STRUCT itself.
  • GEOGRAPHY values come through as their text form instead of an opaque object.
  • INTERVAL columns are handled explicitly — the Google client library throws on them, and rather than let that take a query down, the raw value is read instead.
  • Repeated and record columns are left out of value-frequency profiling, because asking for the distinct values of an array is not a question BigQuery answers.

A dataset is what other engines call a database

The vocabulary mismatch is worth stating plainly, because it decides what you type. The connection binds a project and a default dataset, and it is the dataset name that appears in the middle of a cross-source reference.

  • Within a single BigQuery connection, write table names unqualified — the job already has your default dataset set. Never write the project.
  • Across sources, the middle part is the dataset.
  • One BigQuery connection reads one project. Joining two projects is a federated query across two connections, not a cross-database join inside one.
-- bq_analytics is the connection, events is the BigQuery dataset
SELECT   s.utm_source, COUNT(*) AS sessions
FROM     bq_analytics.events.sessions  s
JOIN     pg_crm.public.customers       c ON c.id = s.customer_id
WHERE    c.plan = 'enterprise'
GROUP BY s.utm_source

Shared by every database connector

True of every database connector

  • Hanya untuk keberangkatan — the agent opens one encrypted connection out to Query Streams. No inbound port to forward, no VPN, no IP allowlist, nothing about your database exposed to the internet.
  • Credentials stay put — the database username and password live on the machine you installed the agent on. Query Streams never receives them and cannot reach your database on its own.
  • Hanya baca, diberlakukan — one statement at a time, SELECT and friends only. A write is rejected on your own machine before it is ever sent to the server, rather than relying on a permission somebody remembered to set.
  • Deploy as many agents as you like — one per site, region or cloud. Every data source they can see arrives in a single dropdown, so nobody has to know which agent hosts what.

What you get once a query is saved

  • Share the capability, not the SQL — a colleague or an outside partner can run your query and change its filters without ever seeing the statement behind it.
  • Filters from either direction — declare them yourself as @variables, or let the connector spot the literal values already sitting in your WHERE clause and offer those as dropdowns.
  • Read it from anywhere — Microsoft Excel, Google Sheets, Power BI, the REST API, AI assistants over MCP, the Query Builder and Nova all read the same saved query.
  • Run several at once — five saved queries into five worksheet tabs, streamed concurrently, however large the results.
  • Join it to anything else you have connected — another database, a business API, or a folder of files, in one read-only statement.

Cross-source SQL

Join BigQuery to the rest of your data

One statement can span BigQuery and your other connections at once. Each source runs only the part it can, streams the result back, and the join happens centrally — the sources never talk to each other and nothing is copied anywhere.

3 connections · 3 agents

Google BigQuery Gudang awan
PostgreSQL Mesin relasional
Stripe Pembayaran & penagihan

One statement

-- nothing copied, nothing merged, nothing scheduled
PILIH   c.region, COUNT(*) AS orders, SUM(i.amount_due) AS invoiced
DARI     bq_analytics.analytics.orders1 f
GABUNG     pg_crmpelanggan umum2       c ON c.id = f.customer_id
GABUNG     penagihan.stripe.invoices3       i ON i.customer = c.stripe_id
KELOMPOKKAN BERDASARKAN c.region
PESAN OLEH invoiced DESC;

The three parts are connection, schema and table — and the connection name is whatever you called it. Illustrative columns; your tables will be your tables. Read-only applies to every piece: SELECT, WITH and EXPLAIN only, with a ceiling on how much any one source may hand over for a single query. Cara kerja kueri terfederasi

Connection details

What BigQuery needs

Reached over
HTTPS to Google's BigQuery API. No port, no listener, nothing inbound
Identified by
A GCP project, and usually a default dataset
Authentication
A service account key — pasted as JSON, or left as a file on the agent's disk and referenced by path
Permissions
Whatever the service account can read, and nothing else. Scoped to the BigQuery API alone
Klien
Google.Cloud.BigQuery.V2 3.10.0, bundled — no native dependency to install
Query timeout
300 seconds by default, which is deliberately generous for analytical work
Hasil
The job completes and the rows are then streamed out, because that is how the jobs API delivers them
Hanya baca
Enforced on the agent before the job is submitted

Query Streams does not cap what BigQuery bills you. There is no dry-run estimate and no bytes-billed ceiling here — a query that scans a large unpartitioned table costs exactly what it would from the console. Partition your tables, and use GCP's own quotas and the service account's grants as the control.

In a cross-source query the middle part of a three-part reference is the BigQuery dataset. A connection called bq_analytics reading the events dataset is written bq_analytics.events.sessions.

Vendor documentation: cloud.google.com

FAQ

Questions about Google BigQuery

Which tools can read Google BigQuery data through Query Streams?

All of them, from one connection: Excel, Google Sheets, MCP, REST API, Airtable, Baserow, SeaTable, Smartsheet, Anvil, Power BI, scheduled alerts and reports. Connect the warehouse once and every surface reads the same read-only connection — there is no per-tool setup and no second copy of the data.

Do I have to open a firewall port to my Google BigQuery warehouse?

No. The Query Streams Network Agent runs inside your network and opens a single outbound encrypted connection. Nothing listens for inbound traffic, no VPN is required, and the warehouse keeps its existing firewall rules.

Can Query Streams change data in Google BigQuery?

No. The agent enforces read-only at the point of execution — one statement at a time, SELECT and friends only. Credentials stay on the agent and are never sent to Query Streams.

What does Query Streams need to connect to Google BigQuery?

A reachable host, a role and its password — the agent carries the driver, so nothing is installed on the warehouse. Reached over: HTTPS to Google's BigQuery API. No port, no listener, nothing inbound. Identified by: A GCP project, and usually a default dataset. Authentication: A service account key — pasted as JSON, or left as a file on the agent's disk and referenced by path. Permissions: Whatever the service account can read, and nothing else. Scoped to the BigQuery API alone.

Can I join Google BigQuery to another database in the same query?

Yes — that is a federated query. One statement can reference Google BigQuery and your other connections at once, written as connection.schema.table. Each source runs only the part it can and streams the result back; the join happens centrally, so the sources never connect to each other and nothing is copied or scheduled. Read-only applies to every piece — SELECT, WITH and EXPLAIN only — and there is a ceiling on how much any one source may hand over for a single query. Federated queries are a plan feature; the federated queries page carries the current source and size limits.

Put BigQuery where the work happens

Install the agent, point it at your warehouse, and pick a destination.

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Hanya baca Hanya untuk keberangkatan Credentials stay on the agent