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Google BigQuery n8n integrationAutomate Google BigQuery with n8n.

The Google BigQuery n8n integration gives a workflow a direct line to the warehouse. The node carries 2 operations, both on the Database resource: run a SQL query, or insert rows into a table. BigQuery has no trigger node, so another step starts the run.

Try the Google BigQuery integration on n8nSee the 2 operations

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Why automate

What does the Google BigQuery n8n integration actually do?

The Google BigQuery n8n integration links an n8n workflow to a Google Cloud data warehouse through a single node and 2 operations, both grouped under the Database resource. One sends a SQL query to a project and hands back the rows. The other writes rows coming out of the workflow into a named table. Everything else, from the dataset to the text of the query, comes from the fields you fill in.

First scenario, the morning numbers. A Schedule Trigger fires, the query aggregates yesterday's orders, and the result lands in Slack before the team opens a single tab. The query sits in the SQL Query field, so changing the metric means editing one line instead of rebuilding anything.

Second scenario, archiving. Rows produced somewhere else, a form response, an incoming call payload, a row read from Google Sheets, end up in BigQuery through the Insert operation. When the incoming item already carries the destination column names, Data Mode set to Auto-Map Input Data is all it takes.

Third scenario, deciding with data. A query returns a customer segment and the workflow branches on it: an email through Gmail, a record updated further down. BigQuery answers the question, n8n acts on the answer.

2 operations is a narrow surface, and it pays to know that before designing around the node. Creating a dataset, listing tables, handling access rights: none of that is in there. The HTTP Request node picks it up, calling the BigQuery REST API with the same Google credential, meaning the connection you saved once inside n8n.

The other limit is about fit. This node is not a transactional database client. For row-by-row reads and writes against a schema you control, Postgres or Supabase sit closer to the job. BigQuery wins when the table is huge and the query does the heavy lifting. One node, one statement, and the warehouse scans the rows instead of your workflow. Still weighing the platform itself? The n8n review goes through it.

Connect

How do you connect Google BigQuery to n8n?

  1. 01

    Pick an authentication method

    The node takes two credential types: OAuth2, recommended because it is more widely available and quicker to set up, or a Service Account, which n8n supports on a limited set of nodes. On n8n Cloud, Managed OAuth2 turns the whole thing into one click: open the credential screen, select Sign in with Google, and nothing needs configuring in the Google Cloud Console.

  2. 02

    Create the project and the consent screen

    Self-hosted instances take the long road, custom OAuth2. Create a Google Cloud Console project, enable the APIs you need, then configure the OAuth consent screen: app name, support email, and an Audience set to Internal for your own Workspace or External for any Google account. Under Branding, add n8n.cloud as an authorized domain, or the domain of your own instance.

  3. 03

    Paste the Client ID and Client Secret

    In APIs and Services, select Create credentials, then OAuth client ID, and choose Web application as the application type. Copy the OAuth Redirect URL shown in your n8n credential and paste it into Authorized redirect URIs. Create the client, then carry the Client ID and Client Secret back into n8n. The credential is saved once and shows up in the node dropdown of every workflow after that.

Actions

What can the Google BigQuery node do?

The Google BigQuery node exposes 2 operations across 1 resources. For each one: the node as you configure it in n8n, the required fields, and our field notes.

Resources × operations matrix
ResourceExecute QueryInsert
Database✓✓

Database

2 operations
01

Execute a SQL query

database.executeQuery

What you see in n8n

Notes & use cases

Sends a SQL statement to BigQuery and returns the result rows as n8n items, one row per item.

Key parameters

  • Project: the Google Cloud project that runs and pays for the job, chosen from the projects where you hold a role.
  • SQL Query: the statement itself, standard SQL by default, in the shape of SELECT * FROM dataset.table LIMIT 100.
  • Dry Run: BigQuery skips the job and reports how many bytes it would process, which is how you price a query before running it.
Use cases
a morning workflow that pulls yesterday's revenue by channel and drops it where the team will actually see it.
02

Insert rows in a table

database.insert

What you see in n8n

Notes & use cases

Writes the items arriving in the node as new rows of an existing BigQuery table, with no SQL to write.

Key parameters

  • Dataset and Table: the destination, both required alongside Project, picked from your BigQuery tree or passed as an expression.
  • Data Mode: Auto-Map Input Data matches incoming property names to destination columns; Map Each Field Below lets you set every value by hand.
  • Skip Invalid Rows: on, a row that breaks the schema is dropped and the rest still land; off, the whole batch fails.
Use cases
an hourly job that appends new sign-ups to an events table so analysts can query them the next morning.
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FAQ

Google BigQuery and n8n, the questions that come next

01Is the Google BigQuery n8n integration free?
Yes on the n8n side. The Google BigQuery node ships with n8n, so there is nothing to install and nothing extra to pay, on n8n Cloud as well as on a self-hosted instance running the Community Edition under its Sustainable Use license. A workflow behaves identically in both. What you consume inside Google Cloud is a separate matter, and this page does not cover it. One habit helps anyway: run the query once with Dry Run enabled, read the byte volume BigQuery reports, and decide with that number in front of you.
02What credentials does the Google BigQuery node need?
Two methods work. OAuth2 is the one to try first, since it is more widely available and takes fewer steps. On n8n Cloud, Managed OAuth2 removes the Google Cloud Console entirely: select Sign in with Google in the credential screen and you are done. Self-hosted users build a custom OAuth2 credential instead, which means a Google Cloud project, the enabled APIs, a configured consent screen, and a Web application OAuth client whose redirect URI matches the one n8n displays. A Service Account is the other route, supported on a limited set of nodes, filled in with the Service Account Email and the Private Key from the downloaded JSON file.
03What are the limits of the BigQuery node in n8n?
The node carries 2 operations on a single Database resource: run a query, insert rows. That is the entire surface. Creating a dataset, editing a schema, listing tables or managing access rights are not in it, and the HTTP Request node covers them by calling the BigQuery REST API with the same Google credential. On the query side, Max Results Per Page sets how many rows come back per page rather than the total, so the LIMIT clause in your SQL stays the thing that really caps the result. Timeout is expressed in milliseconds, and the call typically returns after around 200 seconds anyway.
04Can a workflow start from Google BigQuery?
No. BigQuery has no trigger node in n8n, so nothing inside the warehouse wakes a workflow on its own. The workflow starts elsewhere and the BigQuery node comes after it. A Schedule Trigger, which fires at a fixed interval, covers most reporting needs: it goes off, the query runs, the rows move on. An n8n Webhook, meaning a URL a third-party service calls, fits when an outside system knows something happened. The trigger of another tool works too, for instance a new row that should be archived. The BigQuery node itself only acts once the workflow reaches it.
05n8n or Make for Google BigQuery?
It depends on four things. Hosting: n8n installs on your own server through Docker or npm, or runs on n8n Cloud, while Make exists only as a hosted platform. Data control follows from that, and for a warehouse holding customer data, keeping the queries inside your own infrastructure often weighs heavily. Cost model: Make bills per operation, something a workflow inserting rows in volume ends up feeling. Visual logic: both draw the flow on a canvas, and there it is mostly a matter of habit. Test both on one real query before closing the decision.
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