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Google BigQuery Integrations

Data & analytics

Automate work across your stack

Put Google BigQuery at the center of your automated work

Run SQL, load files into BigQuery tables and start workflows when the connected account creates a job in Google BigQuery.

View Google BigQuery templatesConnection guide
  • ✓Visual Workflow builder
  • ✓Schema-aware field mapping
  • ✓Retries and failure recovery
  • ✓Run audit and observability
  • ✓Reusable connected accounts
  • ✓Triggers, Actions, filters, and Paths

Connect Google BigQuery with another app

Choose an app

Google Drive logoGoogle Drive1 template · File managementSlack logoSlack1 template · Team communication
Google BigQuery logoGoogle BigQuery + FastHook + your tools

Ready-made Google BigQuery Workflows

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Google BigQuery + Google Drive

Google Drive · Google BigQuery

Load new Google Drive CSV files into BigQuery

Connect Drive and BigQuery using a Google account registered for BigQuery OAuth testing. Select a dedicated Drive folder and an existing table with a compatible schema. Use the same Google Cloud project in Project ID, Configuration → Destination table, and Job reference. After the first poll, upload a new UTF-8 CSV up to 5 MiB with one header row. FastHook downloads the File and submits an append job with an ID derived from the Drive file. Check the job status in BigQuery to confirm the load.

  1. 1. Google Drive · New file in Google Drive
  2. 2. FastHook · Only CSV files
  3. 3. Google Drive · Download CSV File
  4. 4. Google BigQuery · Start a CSV load job
View template →Use template

Google BigQuery + Slack

Google BigQuery · Slack

Notify Slack about new BigQuery jobs

Connect BigQuery using a Google account registered for OAuth testing, then connect Slack. Enter the Google Cloud project to watch and select a private Slack channel. Run a new query from the same Google account after the first poll. FastHook sends its job ID, location and observed state. The message omits SQL and query results.

  1. 1. Google BigQuery · New BigQuery job
  2. 2. Slack · Send job metadata to Slack
View template →Use template
01

Connect Google BigQuery

Connect a Google account in FastHook and select your Google Cloud project. Google Cloud IAM controls which datasets, tables and jobs the account can access. Enable the BigQuery APIs you intend to use in the appropriate project.

02

Map your Workflow data

Connect your accounts and map the required fields from earlier steps. Test with data you control before activation.

03

Before you activate

The Google OAuth app is in Testing. Only registered test users can connect, and their Google authorization expires after seven days. The first poll records a baseline. Later polls watch jobs started by the connected account in the selected project. In BigQuery Sandbox, queries and batch loads are available within sandbox quotas. Streaming inserts, DML and Data Transfer Service require a billing-enabled project. List and result Actions return a page. Use the next page token to continue. A query can return before completion. Use Get SQL query results with its job ID and location. Set maximum bytes billed for queries that scan data. Google applies service quotas and IAM restrictions to Actions. The File Action uses multipart upload. The BigQuery Storage streaming APIs use a separate gRPC transport. Disconnect the account in FastHook to revoke its Google authorization.

Build your Workflow

What you can automate with Google BigQuery

  • Run parameterized GoogleSQL queries and retrieve additional result pages
  • Create and manage datasets, tables, routines and jobs
  • Load a CSV or another supported data File up to 5 MiB into a table
  • Start a workflow when the connected Google account creates a new job
  • Manage transfers, connections, reservations, migration workflows, sharing and data policies through their REST APIs

FAQ

Google BigQuery automation questions

Connection guide →
How do I connect Google BigQuery?

Connect a Google account in FastHook and select your Google Cloud project. Google Cloud IAM controls which datasets, tables and jobs the account can access. Enable the BigQuery APIs you intend to use in the appropriate project.

What can I automate with Google BigQuery?

Run parameterized GoogleSQL queries and retrieve additional result pages. Create and manage datasets, tables, routines and jobs. Load a CSV or another supported data File up to 5 MiB into a table. Start a workflow when the connected Google account creates a new job. Manage transfers, connections, reservations, migration workflows, sharing and data policies through their REST APIs

What should I check before activating?

The Google OAuth app is in Testing. Only registered test users can connect, and their Google authorization expires after seven days. The first poll records a baseline. Later polls watch jobs started by the connected account in the selected project. In BigQuery Sandbox, queries and batch loads are available within sandbox quotas. Streaming inserts, DML and Data Transfer Service require a billing-enabled project. List and result Actions return a page. Use the next page token to continue. A query can return before completion. Use Get SQL query results with its job ID and location. Set maximum bytes billed for queries that scan data. Google applies service quotas and IAM restrictions to Actions. The File Action uses multipart upload. The BigQuery Storage streaming APIs use a separate gRPC transport. Disconnect the account in FastHook to revoke its Google authorization.

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Put Google BigQuery at the center of your automated work

Choose a ready-made Google BigQuery Workflow, connect your accounts, and test it before activation.

Explore Google BigQuery Workflow templates