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Google Cloud BigQuery Connector

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Description​

Google Cloud BigQuery is Google Cloud's fully managed, petabyte-scale, and cost-effective analytics data warehouse that enables running analytics over vast amounts of data in near real time.

API Documentation​

This component was built using the Google Cloud BigQuery API.

The Google Cloud BigQuery component supports receiving notifications through Google Cloud Pub/Sub. This enables integrations to respond to events such as table changes, query completions, and data insertions.

Setting Up Pub/Sub Notifications for BigQuery​

  1. In the Google Cloud console, navigate to the Pub/Sub page (Navigation Menu > More Products > Analytics > Pub/Sub).
  2. In the Topics page, click Create Topic.
    • Enter a Topic ID (e.g., bigquery-notifications).
    • Leave the default values for the remaining options, and then click Create.
  3. Create a subscription for the topic:
    • Navigate to Pub/Sub > Subscriptions and click Create subscription.
    • Enter a Subscription ID.
    • For Select a Cloud Pub/Sub topic, select the topic created in the previous step.
    • Under Delivery type, select Push and enter the webhook URL from the integration's trigger configuration.
    • Click Create.
  4. Configure BigQuery to publish notifications to the Pub/Sub topic using the BigQuery API or the Google Cloud console.

When messages are published to the configured topic, they will be sent to the push endpoint configured in the integration trigger.

Connections​

Private Key​

key: googleServiceAccount
InputNotesExample
Client Email

The email address of the client to connect.

someone@example.com
Private Key

The private key of the client to connect.

Scopes

Space delimited listing of scopes. https://developers.google.com/identity/protocols/oauth2/scopes#bigquery

https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only

OAuth 2.0​

key: oauth2

The Google BigQuery component authenticates requests through the Google Cloud Platform (GCP) OAuth 2.0 service. A GCP OAuth 2.0 app is required so the integration can authenticate and perform Google BigQuery tasks on the customer's behalf.

Prerequisites​

Setup Steps​

  1. Open up the Google BigQuery Console
  2. Click CREATE PROJECT to create a new GCP project, or select an existing project.
  3. The system will prompt to enable Google BigQuery for the project. Click ENABLE.
  4. On the sidebar, select Credentials.
  5. An OAuth 2.0 app includes a "Consent Screen" (the page that asks "Do you want to allow (Your Company) to access Google BigQuery on your behalf?"). Click CONFIGURE CONSENT SCREEN.
    1. The app will be externally available to end users, so choose a User Type of External.
    2. Fill out the OAuth consent screen with an app name (company or product name), support email, app logo, domain, etc.
    3. Domains can be ignored for now.
    4. On the next page, add these scopes to the app (these may not all be necessary, and should match the scopes in the connection definition):
      • https://www.googleapis.com/auth/bigquery
      • https://www.googleapis.com/auth/bigquery.insertdata
      • https://www.googleapis.com/auth/cloud-platform
      • https://www.googleapis.com/auth/cloud-platform.read-only
      • https://www.googleapis.com/auth/devstorage.full_control
      • https://www.googleapis.com/auth/devstorage.read_only
      • https://www.googleapis.com/auth/devstorage.read_write
    5. Enter some test users for testing purposes. The app will only work for those testing users until it is "verified" by Google. When ready for verification (verification includes the privacy policy statement, etc), click PUBLISH APP on the OAuth consent screen. This will allow end users to authorize the integration to access their Google BigQuery data.
  6. Once the "Consent Screen" is configured, open the Credentials page from the sidebar again.
  7. Click +CREATE CREDENTIALS and select OAuth client ID.
    1. Under Application type select Web application.
    2. Under Authorized redirect URIs enter the OAuth 2.0 callback URL: https://oauth2.prismatic.io/callback
    3. Click CREATE.
  8. Take note of the Client ID and Client Secret that are generated.

Configure the Connection​

Create a connection of type OAuth 2.0 and enter:

  • Client ID: Enter the Client ID from the OAuth application
  • Client Secret: Enter the Client Secret from the OAuth application
  • Scopes: The default Google BigQuery scopes should be kept:
https://www.googleapis.com/auth/bigqueryView and manage data in Google BigQuery and see the email address for the Google Account
https://www.googleapis.com/auth/bigquery.insertdataInsert data into Google BigQuery
https://www.googleapis.com/auth/cloud-platformSee, edit, configure, and delete Google Cloud data and see the email address for the Google Account.
https://www.googleapis.com/auth/cloud-platform.read-onlyView data across Google Cloud services and see the email address of the Google Account
https://www.googleapis.com/auth/devstorage.full_controlManage data and permissions in Cloud Storage and see the email address for the Google Account
https://www.googleapis.com/auth/devstorage.read_onlyView data in Google Cloud Storage
https://www.googleapis.com/auth/devstorage.read_writeManage data in Cloud Storage and see the email address of the Google Account

App Verification​

Google requires OAuth apps that request access to user data to pass a verification review before being deployed at scale. This process ensures the app complies with Google's API Services User Data Policy, accurately represents its functionality, and handles user data responsibly.

Google OAuth apps pass through three stages before they are ready for production use.

Testing (unpublished): The app is only accessible to users manually added as test users in the OAuth consent screen. Up to 100 test users are allowed — all other users receive an error. This is the expected state during initial development.

Published, unverified: After publishing the app, all Google users can authenticate. However, for sensitive scopes, users see a "This app isn't verified" warning. Users can proceed by clicking Advanced → Go to [app name] (unsafe), but this warning reduces trust and may be blocked by organizations with strict Google Workspace policies.

Verified: Google has reviewed and approved the app. No warning is shown. Verification is required before deploying to production users.

Publishing the App​

Publishing is required before any users outside the test list can authenticate:

  1. In the Google Cloud Console, navigate to APIs & Services → OAuth consent screen
  2. Click PUBLISH APP and confirm

Requesting Verification​

The scopes used by this component are classified as sensitive by Google. Submitting for verification removes the "This app isn't verified" warning:

  1. On the OAuth consent screen, click Prepare for verification
  2. Provide a privacy policy URL, authorized domain, and app logo
  3. Submit for review — Google typically responds within several weeks

Refer to Google's OAuth consent screen documentation for the full verification requirements.

InputNotesExample
Authorize URL

The Authorization URL for Google BigQuery.

https://accounts.google.com/o/oauth2/v2/auth?access_type=offline&prompt=consent
Client ID

The Google BigQuery app's Client Identifier.

Client Secret

The Google BigQuery app's Client Secret.

Scopes

Space delimited listing of scopes. https://developers.google.com/identity/protocols/oauth2/scopes#bigquery

https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigquery.insertdata https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/devstorage.full_control https://www.googleapis.com/auth/devstorage.read_only https://www.googleapis.com/auth/devstorage.read_write
Token URL

The Token URL for Google BigQuery.

https://oauth2.googleapis.com/token

Triggers​

New Jobs​

Checks for newly created jobs in BigQuery on a recurring schedule. Jobs expose only a creation time, so this detects new jobs since the last run, not changes to existing ones. | key: pollChangesTrigger

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345

This trigger polls Google BigQuery for jobs created in a specified project since the last run, on a configured schedule. BigQuery jobs expose only a creation time and have no modification timestamp, so this trigger detects newly created jobs ordered by creation time. It does not detect status changes on existing jobs. Use it to react to query, load, copy, and export activity as new jobs appear in a project.

How It Works​

  1. The trigger runs on the configured schedule and records the time of each run as its state.
  2. On the first run, it bootstraps from the current time so that historical jobs do not flood the first poll. Only jobs created after that point are surfaced on subsequent runs.
  3. On each run, it fetches jobs created since the last recorded poll time, ordered newest-first by creation time.
  4. State is advanced only after a run fully drains the available jobs, so no created job is skipped between polls.

When a single poll returns more jobs than the page cap allows, the response is treated as truncated. Because BigQuery serves jobs newest-first with no modification timestamp, the trigger walks backward through the backlog on the following runs (draining older jobs first) instead of advancing the lower time bound. This prevents the older, not-yet-fetched portion of the backlog from being dropped. Once the backlog is fully drained, the trigger advances its state to the point captured when draining began, and jobs created during the drain are picked up by the next regular poll.

Returned Data​

The trigger returns an object with one array under data:

  • data.created holds the BigQuery job records created since the last poll.

Fields shown are representative. The full response object includes additional properties.

Example Response (Polling)
{
  "data": {
    "created": [
      {
        "kind": "bigquery#job",
        "id": "my-project:US.job_abc123def456",
        "jobReference": {
          "projectId": "my-project",
          "jobId": "job_abc123def456",
          "location": "US"
        },
        "status": {
          "state": "DONE"
        },
        "statistics": {
          "creationTime": "1640995200000",
          "startTime": "1640995205000",
          "endTime": "1640995210000"
        },
        "user_email": "user@example.com"
      }
    ]
  }
}

Notes​

  • The first poll establishes a baseline from the current time and does not return historical jobs.
  • When a poll exceeds the page cap, the backlog is drained across multiple subsequent polls before the state advances, so delivery may lag during high-volume periods.
  • BigQuery API quotas and rate limits apply. Very frequent schedules may approach those limits in busy projects.
Example Payload for New Jobs⤓
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PubSub Notification​

Receive PubSub notifications from Google Cloud when events occur. | key: myTrigger

Data Sources​

Select Dataset​

A picklist of datasets in the specified project. | key: selectDataset | type: picklist

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Example Payload for Select Dataset⤓
Loading…

Select Job​

A picklist of jobs in the specified project. | key: selectJob | type: picklist

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Example Payload for Select Job⤓
Loading…

Select Model​

A picklist of models in the specified dataset. | key: selectModel | type: picklist

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Example Payload for Select Model⤓
Loading…

Select Project​

A picklist of projects in your Google Cloud account. | key: projectsNames | type: picklist

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Example Payload for Select Project⤓
Loading…

Select Routine​

A picklist of routines in the specified dataset. | key: selectRoutine | type: picklist

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Example Payload for Select Routine⤓
Loading…

Select Table​

A picklist of tables in the specified dataset. | key: tablesNames | type: picklist

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Example Payload for Select Table⤓
Loading…

Actions​

Cancel Job​

Requests that a job be cancelled. | key: cancelJob

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Job ID

The unique identifier for the job.

job_abc123xyz
Location

The geographic location where the dataset should reside. See https://cloud.google.com/bigquery/docs/locations for supported locations.

US
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Create Dataset​

Creates a new empty dataset. | key: createDataset

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset Reference

A reference that identifies the dataset.

{"datasetId":"string","projectId":"string"}
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Create Job​

Starts a new asynchronous job. | key: createJob

InputNotesExample
Additional Fields

Additional optional fields.

Configuration

Required. Describes the job configuration.

Reference to the Google docs for this input. https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfiguration
Connection

The Google Cloud BigQuery connection to use.

Job Reference

Optional. Reference describing the unique-per-user name of the job.

{"projectId":"string","jobId":"string","location":"string"}
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Create Routine​

Creates a new routine in the dataset. | key: createRoutine

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Definition Body

Required. The body of the routine. For functions, this is the expression in the AS clause. If language=SQL, it is the substring inside (but excluding) the parentheses. For example, for the function created with the following statement: CREATE FUNCTION JoinLines(x string, y string) as (concat(x, '\n', y)) The definitionBody is concat(x, '\n', y) (\n is not replaced with linebreak). If language=JAVASCRIPT, it is the evaluated string in the AS clause. For example, for the function created with the following statement: CREATE FUNCTION f() RETURNS STRING LANGUAGE js AS 'return '\n';\n'The definitionBody is return '\n';\n Note that both \n are replaced with linebreaks.

concat(x, '\n', y)
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Routine Reference

Reference describing the ID of this routine.

{"projectId":"string","datasetId":"string","routineId":"string"}
Routine Type

The type of routine. One of ROUTINE_TYPE_UNSPECIFIED / SCALAR_FUNCTION / PROCEDURE / TABLE_VALUED_FUNCTION

SCALAR_FUNCTION

Create Table​

Creates a new, empty table in the dataset. | key: createTable

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset ID

Dataset ID of the table to update.

my_dataset
Project ID

Project ID of the table to update.

my-project-12345
Table Reference

Reference describing the ID of this routine.

{"projectId":"string","datasetId":"string","tableId":"string"}

Delete Dataset​

Deletes the dataset specified by the datasetId value. Before you can delete a dataset, you must delete all its tables, either manually or by specifying deleteContents. Immediately after deletion, you can create another dataset with the same name. | key: deleteDataset

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Delete Job​

Requests the deletion of the metadata of a job. | key: deleteJob

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Job ID

The unique identifier for the job.

job_abc123xyz
Location

The geographic location where the dataset should reside. See https://cloud.google.com/bigquery/docs/locations for supported locations.

US
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Delete Model​

Deletes the model specified by model ID from the dataset. | key: deleteModel

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Model ID

The unique identifier for the model.

my_model
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Delete Routine​

Deletes the routine specified by routine ID from the dataset. | key: deleteRoutine

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Routine ID

The unique identifier for the routine.

my_routine

Delete Table​

Deletes the table specified by table ID from the dataset. | key: deleteTable

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

Dataset ID of the table to delete.

my_dataset
Project ID

Project ID of the table to delete.

my-project-12345
Table ID

Table ID of the table to delete.

my_table

Get Dataset​

Returns the dataset specified by datasetID. | key: getDataset

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Get Job​

Returns information about a specific job. | key: getJob

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Job ID

The unique identifier for the job.

job_abc123xyz
Location

The geographic location where the dataset should reside. See https://cloud.google.com/bigquery/docs/locations for supported locations.

US
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Get Model​

Gets the specified model resource by model ID. | key: getModel

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Model ID

The unique identifier for the model.

my_model
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Get Policy​

Gets the access control policy for a resource. | key: getPolicy

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Options

OPTIONAL: A GetPolicyOptions object for specifying options to tables.getIamPolicy.

{"requestedPolicyVersion":"integer"}
Resource

The resource for which the policy is being requested. See Resource names for the appropriate value for this field.

projects/my-project/datasets/my-dataset/tables/my-table

Get Query Job Results​

Receives the results of a query job. | key: getQueryJobResult

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Job ID

The unique identifier for the job.

job_abc123xyz
Location

The geographic location where the dataset should reside. See https://cloud.google.com/bigquery/docs/locations for supported locations.

US
Pagination

Page navigation controls for the results.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Timeout (ms)

Specifies the maximum amount of time, in milliseconds, that the client is willing to wait for the query to complete. By default, this limit is 10 seconds (10,000 milliseconds). If the query is complete, the jobComplete field in the response is true. If the query has not yet completed, jobComplete is false. You can request a longer timeout period in the timeoutMs field. However, the call is not guaranteed to wait for the specified timeout; it typically returns after around 200 seconds (200,000 milliseconds), even if the query is not complete. If jobComplete is false, you can continue to wait for the query to complete by calling the getQueryResults method until the jobComplete field in the getQueryResults response is true.

10000

Get Routine​

Gets the specified routine resource by routine ID. | key: getRoutine

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Read Mask

If set, only the Routine fields in the field mask are returned in the response. If unset, all Routine fields are returned. This is a comma-separated list of fully qualified names of fields. Example: 'user.displayName,photo'.

user.displayName,photo
Routine ID

The unique identifier for the routine.

my_routine

Get Service Account​

Receives the service account for a project used for interactions with Google Cloud KMS | key: getServiceAccount

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Get Table​

Gets the specified table resource by table ID. | key: getTable

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

Dataset ID of the requested table.

my_dataset
Project ID

Project ID of the requested table.

my-project-12345
Selected Fields

tabledata.list of table schema fields to return (comma-separated). If unspecified, all fields are returned. A fieldMask cannot be used here because the fields will automatically be converted from camelCase to snake_case and the conversion will fail if there are underscores. Since these are fields in BigQuery table schemas, underscores are allowed.

Table ID

Table ID of the requested table.

my_table
View

Optional. Specifies the view that determines which table information is returned. By default, basic table information and storage statistics (STORAGE_STATS) are returned. One of TABLE_METADATA_VIEW_UNSPECIFIED / BASIC / STORAGE_STATS / FULL

FULL

List Datasets​

Lists all datasets in the specified project to which the user has been granted the READER dataset role. | key: listDatasets

InputNotesExample
All

When true, lists all datasets, including hidden ones.

false
Connection

The Google Cloud BigQuery connection to use.

Fetch All

When true, automatically fetches all pages of results using pagination.

false
Filter

An expression for filtering the results of the request by label. The syntax is 'labels.<name>[:<value>]'. Multiple filters can be ANDed together by connecting with a space. Example: 'labels.department:receiving labels.active'. See Filtering datasets using labels for details.

labels.department:receiving labels.active
Pagination

Page navigation controls for the results.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345

List Jobs​

Lists all jobs that you started in the specified project. | key: listJobs

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Fetch All

When true, automatically fetches all pages of results using pagination.

false
Filters

Optional query controls to sort and refine the results.

Pagination

Page navigation controls for the results.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345
State Filter

Filter for job state. Valid values of this enum field are: DONE, PENDING, RUNNING.

["DONE", "RUNNING"]

List Models​

Lists all models in the specified dataset. Requires the READER dataset role. After retrieving the list of models, you can get information about a particular model by calling the models.get method. | key: listModels

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Fetch All

When true, automatically fetches all pages of results using pagination.

false
Pagination

Page navigation controls for the results.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345

List Projects​

Lists projects to which the user has been granted any project role. | key: listProjects

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Fetch All

When true, automatically fetches all pages of results using pagination.

false
Pagination

Page navigation controls for the results.

List Routines​

Lists all routines in the specified dataset. | key: listRoutines

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Fetch All

When true, automatically fetches all pages of results using pagination.

false
Filter

An expression for filtering the results of the request by label. The syntax is 'labels.<name>[:<value>]'. Multiple filters can be ANDed together by connecting with a space. Example: 'labels.department:receiving labels.active'. See Filtering datasets using labels for details.

labels.department:receiving labels.active
Pagination

Page navigation controls for the results.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Read Mask

If set, only the Routine fields in the field mask are returned in the response. If unset, all Routine fields are returned. This is a comma-separated list of fully qualified names of fields. Example: 'user.displayName,photo'.

user.displayName,photo

List Table Data (Deprecated)​

Lists the content of a table in rows. Note: This action now uses jobs.query API as the tabledata.list API has been deprecated by Google. | key: listTableData

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Pagination

Page navigation controls for the results.

Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Selected Fields

Subset of fields to return, supports select into sub fields. Example: selectedFields = 'a,e.d.f';

Table ID

The unique identifier for the table.

my_table

List Tables​

Lists all tables in the specified dataset. | key: listTables

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Dataset ID

Dataset ID of the tables to list.

my_dataset
Fetch All

When true, automatically fetches all pages of results using pagination.

false
Pagination

Page navigation controls for the results.

Project ID

Project ID of the tables to list.

my-project-12345

Patch Table​

Patch information in an existing table. | key: patchTable

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset ID

Dataset ID of the table to patch.

my_dataset
Project ID

Project ID of the table to patch.

my-project-12345
Table ID

Table ID of the table to patch.

my_table
Table Reference

Reference describing the ID of this routine.

{"projectId":"string","datasetId":"string","tableId":"string"}

Query Job​

Runs a BigQuery SQL query synchronously and returns query results if the query completes within a specified timeout. | key: queryJob

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Max Results

The maximum number of results to return in a single response page. Leverage the page tokens to iterate through the entire collection.

100
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Query

Required. A query string to execute, using Google Standard SQL or legacy SQL syntax. Example: 'SELECT COUNT(f1) FROM myProjectId.myDatasetId.myTableId'.

SELECT COUNT(f1) FROM myProjectId.myDatasetId.myTableId

Raw Request​

Send raw HTTP request to Google Cloud BigQuery | key: rawRequest

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Data

The HTTP body payload to send to the URL.

{"exampleKey": "Example Data"}
File Data

File Data to be sent as a multipart form upload.

[{key: "example.txt", value: "My File Contents"}]
File Data File Names

File names to apply to the file data inputs. Keys must match the file data keys above.

Form Data

The Form Data to be sent as a multipart form upload.

[{"key": "Example Key", "value": new Buffer("Hello World")}]
Header

A list of headers to send with the request.

User-Agent: curl/7.64.1
Max Retry Count

The maximum number of retries to attempt. Specify 0 for no retries.

0
Method

The HTTP method to use.

Query Parameter

A list of query parameters to send with the request. This is the portion at the end of the URL similar to ?key1=value1&key2=value2.

Response Type

The type of data you expect in the response. You can request json, text, or binary data.

json
Retry On All Errors

If true, retries on all erroneous responses regardless of type. This is helpful when retrying after HTTP 429 or other 3xx or 4xx errors. Otherwise, only retries on HTTP 5xx and network errors.

false
Retry Delay (ms)

The delay in milliseconds between retries. This is used when 'Use Exponential Backoff' is disabled.

0
Timeout

The maximum time that a client will await a response to its request

2000
URL

Input the path only (/projects/{projectId}/jobs), The base URL is already included (https://bigquery.googleapis.com/bigquery/{version}). For example, to connect to https://bigquery.googleapis.com/bigquery/v2/projects/{projectId}/jobs, only /projects/{projectId}/jobs is entered in this field.

/projects/{projectId}/jobs
Use Exponential Backoff

Specifies whether to use a pre-defined exponential backoff strategy for retries. When enabled, 'Retry Delay (ms)' is ignored.

false
API Version

The API version to use. This is used to construct the base URL for the request.

v2

Set Policy​

Sets the access control policy on the specified resource. | key: setPolicy

InputNotesExample
Connection

The Google Cloud BigQuery connection to use.

Policy

The complete policy to be applied to the resource. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them.

Reference to the Google docs for this input. https://cloud.google.com/bigquery/docs/reference/rest/v2/Policy
Resource

The resource for which the policy is being requested. See Resource names for the appropriate value for this field.

projects/my-project/datasets/my-dataset/tables/my-table
Update Mask

OPTIONAL: A FieldMask specifying which fields of the policy to modify. Only the fields in the mask will be modified. If no mask is provided, the following default mask is used: paths: 'bindings, etag' This is a comma-separated list of fully qualified names of fields. Example: 'user.displayName,photo'.

user.displayName,photo

Table Data Insert All​

Streams data into BigQuery one record at a time without needing to run a load job. | key: tableDataInsertAll

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Rows

The complete policy to be applied to the resource. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them.

[{"insertId":"string","json":"follow this structure: https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#google.protobuf.Struct"}]
Table ID

The unique identifier for the table.

my_table

Update Dataset​

Updates information in an existing dataset. The update method replaces the entire dataset resource, whereas the patch method only replaces fields that are provided in the submitted dataset resource. | key: updateDataset

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Dataset Reference

A reference that identifies the dataset.

{"datasetId":"string","projectId":"string"}
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Update Model​

Patch specific fields in the specified model. | key: updateModel

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Model ID

The unique identifier for the model.

my_model
Model Reference

Unique identifier for this model.

{"projectId":"string","datasetId":"string","modelId":"string"}
Project ID

The unique identifier for the Google Cloud project.

my-project-12345

Update Routine​

Updates information in an existing routine. | key: updateRoutine

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset ID

The unique identifier for the dataset.

my_dataset
Definition Body

Required. The body of the routine. For functions, this is the expression in the AS clause. If language=SQL, it is the substring inside (but excluding) the parentheses. For example, for the function created with the following statement: CREATE FUNCTION JoinLines(x string, y string) as (concat(x, '\n', y)) The definitionBody is concat(x, '\n', y) (\n is not replaced with linebreak). If language=JAVASCRIPT, it is the evaluated string in the AS clause. For example, for the function created with the following statement: CREATE FUNCTION f() RETURNS STRING LANGUAGE js AS 'return '\n';\n'The definitionBody is return '\n';\n Note that both \n are replaced with linebreaks.

concat(x, '\n', y)
Project ID

The unique identifier for the Google Cloud project.

my-project-12345
Routine Reference

Reference describing the ID of this routine.

{"projectId":"string","datasetId":"string","routineId":"string"}
Routine Type

The type of routine. One of ROUTINE_TYPE_UNSPECIFIED / SCALAR_FUNCTION / PROCEDURE / TABLE_VALUED_FUNCTION

SCALAR_FUNCTION

Update Table​

Updates information in an existing table. | key: updateTable

InputNotesExample
Additional Fields

Additional optional fields.

Connection

The Google Cloud BigQuery connection to use.

Dataset ID

Dataset ID of the table to update.

my_dataset
Project ID

Project ID of the table to update.

my-project-12345
Table ID

Table ID of the table to update.

my_table
Table Reference

Reference describing the ID of this routine.

{"projectId":"string","datasetId":"string","tableId":"string"}

Changelog​

2026-07-15​

Grouped related optional inputs into structured objects across BigQuery actions to reduce clutter in the configuration UI

  • Grouped the pagination inputs on the List Jobs, List Datasets, List Models, List Routines, List Projects, and List Tables actions into a Pagination group, with Fetch All remaining a top-level toggle
  • Grouped the pagination inputs on Get Query Job Results and List Table Data (Deprecated) into a Pagination group
  • Grouped the List Jobs filter inputs into a Filters group, with State Filter remaining top-level
  • Grouped the optional body fields on Create Dataset, Update Dataset, Create Table, Patch Table, Update Table, Update Model, Create Routine, Update Routine, Create Job, Query Job, and Table Data Insert All into an Additional Fields group

2026-06-09​

Added New Jobs polling trigger that surfaces BigQuery jobs created

2026-05-14​

Added bulk pagination support to list actions across datasets, jobs, models, projects, routines, and tables, allowing users to fetch all pages of results in a single action invocation

2026-04-30​

Updated spectral version

2026-04-07​

Added global debug support across all actions for improved troubleshooting

2026-03-05​

Added inline data source for jobs to enable dynamic dropdown selection

2026-02-26​

Added inline data sources for datasets, models, and routines to enable dynamic dropdown selection

2026-02-12​

Improved documentation and labeling

2026-01-08​

Fixed input handling for actions that accept JSON data as a reference.