Google BigQuery Integration

Query and manage Google BigQuery datasets, tables, jobs, and routines — run SQL and stream rows. Use Google BigQuery as a workflow source of truth for querying, inserting, updating, and syncing data. Start from Watch Jobs, then connect database actions like Get Dataset and Get Job.

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Supported triggers and actions for Google BigQuery integration

Triggers can detect changes in Google BigQuery and run actions that can create, search or modify data inside Google BigQuery automatically [3] — exactly how you set them.

Watch JobsTriggers when a new BigQuery job is created.
Get DatasetReturns a single dataset.
Get JobReturns a single job.
Get ModelReturns a single model.
Get Query ResultsReturns results for a query job.
Get RoutineReturns a single routine.
Get Routine IAM PolicyReturns the IAM policy for a routine.
Get Row Access PolicyReturns a single row access policy.
Get Row Access Policy IAM PolicyReturns the IAM policy for a row access policy.
Get Service AccountReturns the project's BigQuery service account.
Get TableReturns a single table.
Get Table IAM PolicyReturns the IAM policy for a table.
List DatasetsRetrieves datasets in a project.
List JobsRetrieves jobs in a project.
List ModelsRetrieves models in a dataset.
List ProjectsRetrieves projects the user can access.
List RoutinesRetrieves routines in a dataset.
List Row Access PoliciesRetrieves row access policies on a table.
List Table DataRetrieves rows from a table.
List TablesRetrieves tables in a dataset.
Cancel JobCancels a running job.
Create DatasetCreates a new dataset.
Create JobCreates a query, load, copy, or extract job.
Create RoutineCreates a new routine.
Create Row Access PolicyCreates a row access policy.
Create TableCreates a new table.
Delete DatasetDeletes a dataset.
Delete JobDeletes the metadata of a completed job.
Delete ModelDeletes a model.
Delete RoutineDeletes a routine.
Delete Row Access Policies in BatchDeletes several row access policies.
Delete Row Access PolicyDeletes a row access policy.
Delete TableDeletes a table.
Edit DatasetUpdates selected fields on a dataset.
Edit ModelUpdates selected fields on a model.
Edit TableUpdates selected fields on a table.
Execute QueryRuns a SQL query.
Insert RowsInserts rows into a table.
Make an API CallPerforms an arbitrary authorized API call.
Set Routine IAM PolicySets the IAM policy for a routine.
Set Table IAM PolicySets the IAM policy for a table.
Test Routine IAM PermissionsTests IAM permissions on a routine.
Test Row Access Policy IAM PermissionsTests IAM permissions on a row access policy.
Test Table IAM PermissionsTests IAM permissions on a table.
Undelete DatasetRestores a recently deleted dataset.
Update DatasetReplaces an existing dataset.
Update RoutineReplaces an existing routine.
Update Row Access PolicyReplaces a row access policy.
Update TableReplaces an existing table.

How to integrate Google BigQuery in automation workflow?

3 simple steps to make your Google BigQuery connected with other apps in automation workflows on WEXTL. Up to 10 minutes integration set up.

STEP 1

Select Google BigQuery in workflow

Pick Google BigQuery app in the workflow builder [4] - just one click to select the app.

STEP 2

Connect Google BigQuery

When prompted, authorize access to Google BigQuery in WEXTL once. Google BigQuery credentials are encrypted and never shared with WEXTL. Authorization will be reused in any future API calls [3].

WEXTL wants to integrate with your Google BigQuery account. Approve?

STEP 3

Build a workflow

Build your workflow with Google BigQuery yourself or with WEXTL® AI Builder. Connect Google BigQuery to any available integrations [5], or use HTTP node to go beyond them.

Google BigQuery

How Google BigQuery connects and integrates with WEXTL

Save and approve your credentials once — WEXTL® stores authorization information encrypted and reuses the authentication whenever Google BigQuery app is called in workflows. That allows your database automations to run without your attention, per defined schedule [7]. Explore documentation [6] for a detailed list of fields that Google BigQuery supports.

Full Google BigQuery API documentation

The following table illustrates what fields Google BigQuery integration require during authorization flow inside our workflow builder [4].

Demonstration of Google BigQuery connection step inside WEXTL Workflow Builder [4].

Field	Type	Help
Client IDRequired	text	OAuth 2.0 Client ID from Google Cloud Console → APIs & Services → Credentials → Web application. Enable the BigQuery API. Add this app's redirect URI.
Client SecretRequired	password	Client secret for the same OAuth client. Verify may return 401 if the access token has aged out — reconnect or wait for the refresh condition to run; Verify deliberately does not refresh first.
Additional Scopes	array	Optional. Extra Google OAuth scopes to request on top of the default ones, one full scope URL per item (for example https://www.googleapis.com/auth/drive). Duplicates of the default scopes are ignored. Use this with your own Client ID, and enable the same scopes on its OAuth consent screen.

Connect Google BigQuery to popular apps on WEXTL

Enter a name of the other app you want to sync with Google BigQuery below. Click on selected app to explore integration options with Google BigQuery. If the app you search for is not available in WEXTL yet, you can use HTTP node [10] instead to integrate it directly.

Example automation with Google BigQuery integration

Select Watch Jobs trigger from Google BigQuery - this will be a starting point in your automation. Whenever event happens in Google BigQuery, workflow will start [7] from Watch Jobs node.

Search for Cancel Job, connect it as the next step of integration sequence. You can use filters between Watch Jobs and Cancel Job, so only important data pass from Watch Jobs to the next step. WEXTL gives you full control over the process of Google BigQuery automation. All parts of workflow are visible on the canvas [4].

Google BigQuery on WEXTL has 49 modules for crafting database automations. Explore Google BigQuery documentation for more.

Google BigQuery

Watch Jobs

Google Firestore

Performs action

Google Firestore

Triggers

Google BigQuery

Cancel Job

Demonstration of how visual Google BigQuery automation [6] looks in WEXTL.

Frequently asked questions

Common questions about automating Google BigQuery in your workflows.

Google BigQuery has 1 trigger, including "Watch Jobs". Triggers are start points of the workflow, and automations may start from them.

Integration has 48 actions, including "Get Dataset", "Get Job", and "Get Model", and more. Refer to our Google BigQuery Documentation for detailed inputs and outputs.

No. Most automations are built visually on the canvas by picking triggers, actions, and mapping fields. You can add custom JavaScript with Functions when advanced logic is needed — but our customers do that in rare cases [4].

Yes. Connections are authenticated per account, credentials are encrypted at rest, and you can enforce approvals and team-level access controls on any automation.

The Google BigQuery integration on WEXTL runs directly on its vendor public API — every field name, authentication method, and rate limit is controlled by Google BigQuery official API and covered in documentation [3]. WEXTL handles the request signing and pagination for you.

Absolutely. Where Google BigQuery provides native webhooks, you can use instant triggers to eliminate polling delay. For custom webhooks, WEXTL offers Custom Webhook node, so you can start automation whenever Google BigQuery or any other service sends event to webhook URL.

WEXTL is an AI-first visual canvas. The platform allows you to describe automation in plain text — and AI will wire nodes for Google BigQuery inside the workflow builder [4]. You can also use built-in workflow nodes (AI Agent, Human in the Loop, Condition, Aggregate Results) in pair with Google BigQuery modules to craft complex integration flows with branches, filtering, and conditional logic. There are absolutely no limits of what you can build.

References

List of sources used to gather information about the Google BigQuery integration.

Google BigQuery integration source code, module specs and credentials implementation.
Internal Google BigQuery integration documentation, structured input and output parameters, scopes, and authentication methods.
Official Google BigQuery vendor API documentation — used to verify field meanings, authentication flows, and anything our own specs don't fully capture.
WEXTL® Visual Workflow Builder, anonymized data from our technical support team.
WEXTL® Apps Library
WEXTL® Google BigQuery App Documentation
WEXTL® Workflow Scheduler
WEXTL® HTTP node documentation

Google BigQuery Integration

Supported triggers and actions for Google BigQuery integration