Action
Google Cloud BigQuery and Google Vertex AI integration
Automate Google Cloud BigQuery + Google Vertex AI workflows
Connect Google Cloud BigQuery and Google Vertex AI to automate data analytics and machine learning workflows. Query datasets, train models, and generate predictions seamlessly within unified automation pipelines.
Free plan availableNo credit cardDeploy in 5 min
Technical overview
What this integration can actually do
This is not a rigid connector between Google Cloud BigQuery and Google Vertex AI. Use native nodes where they already exist, then cover edge cases with webhook, polling, HTTP Request, or JavaScript in the same scenario.
0 triggers and 6 actions across Google Cloud BigQuery and Google Vertex AI
Gets data from
native app events, webhooks, and scheduled checks
Can do
Analyze File: Gemini and Count Tokens for Content Generation: Gemini, plus 4 more actions
Works via
Native nodes, Webhooks, Polling, HTTP Request, JavaScript
Customizable with
field mapping, filters, branching, retries, dedupe logic, and custom API or JavaScript steps.
Capabilities
Triggers & Actions
Every event and operation available when connecting Google Cloud BigQuery and Google Vertex AI — from both apps.
Action
Count Tokens for Content Generation: Gemini
Action
Generate Content (Advanced): Gemini
Action
Generate Video: Veo
Action
Get Result of Generate Video: Veo
Action
Send Chat Promt: Gemini
Production readiness
Production workflow controls
Use these controls when a workflow needs to stay stable after launch, not just pass a happy-path test.
01
Retry failed API calls
Automatically retry temporary failures before a run is marked as failed.
02
Handle 429 / rate-limit responses
Pause, back off, and continue the workflow safely when an upstream API throttles requests.
03
Add fallback branches for missing fields
Route incomplete payloads into a safe branch instead of letting the main scenario break.
04
Prevent duplicates with lookup-before-create logic
Check whether a record already exists before creating a new one in the destination system.
05
Use JavaScript to normalize dates, phone numbers, tags, and statuses
Clean and standardize values before mapping them into downstream fields.
06
Store execution logs for debugging
Keep a trace of what happened in every run so production issues are easier to inspect.
07
Route failed runs to email or a database
Notify the team or save failures for follow-up when a run cannot complete successfully.
08
Manually rerun failed executions
Replay a failed run after the issue is fixed without rebuilding the scenario from scratch.
Example payload
See what the workflow receives and returns
Show one real event and one real result so technical users can understand the payload shape before they connect accounts or customize the scenario.
Source event
JSON{"event": "client_added","client": {"id": "client_123","firstName": "Alex","lastName": "Smith","status": "active","tags": ["online-coaching"]}}Scenario result
JSON{"target": "wix_contact","operation": "upsert","dedupeBy": "email","status": "created"}Setup
Connect both apps in 3 steps
No developer needed. From credentials to live workflow in under 10 minutes.
01
Connect Google Cloud BigQuery
Authenticate Google Cloud BigQuery in Latenode's Credentials panel. You'll need access to your Google Cloud BigQuery account and permissions to create connections.
02
Connect Google Vertex AI
Add Google Vertex AI credentials (OAuth or API key, depending on the app). Latenode stores credentials securely and never saves your passwords.
03
Build and go live
Pick a trigger and an action, test with real data, then toggle your workflow to Live — done.
What would you like to do with Google Cloud BigQuery and Google Vertex AI?
Yes! Latenode provides a native integration between Google Cloud BigQuery and Google Vertex AI. You can connect them in minutes using our visual workflow builder — no coding required.
Use cases
Explore each app
Start from either hub, then mix triggers and actions with the rest of your stack.
About Google Cloud BigQuery
Google Cloud BigQuery is a fully-managed data warehouse that enables fast SQL querying and analysis of large datasets. It offers real-time analytics, scalable storage, and powerful data management capabilities, allowing businesses to transform and gain insights from their data efficiently. Integrated with a robust set of machine learning and visualization tools, BigQuery simplifies complex data processing, making it an essential solution for data-driven decision-making.
Learn morePopular Google Cloud BigQuery pairs
Google Cloud BigQuery + AI ModelsGoogle Cloud BigQuery + DataGoogle Cloud BigQuery + ActiveCampaignGoogle Cloud BigQuery + AI AgentGoogle Cloud BigQuery + AirtableGoogle Cloud BigQuery + AsanaGoogle Cloud BigQuery + DatadogGoogle Cloud BigQuery + CalendlyGoogle Cloud BigQuery + DropboxGoogle Cloud BigQuery + Close CRM
About Google Vertex AI
Google Vertex AI is a unified machine learning platform designed to accelerate the deployment and management of AI models. It simplifies the process of building, training, and scaling machine learning models with features like AutoML, robust support for custom training, and tools for model monitoring and versioning. Vertex AI provides seamless integration with other Google Cloud services, facilitating data handling and workflow automation, helping businesses to harness the power of AI efficiently and effectively.
Learn morePopular Google Vertex AI pairs
Google Vertex AI + AI ModelsGoogle Vertex AI + ERP & Business SuiteGoogle Vertex AI + ClaudeGoogle Vertex AI + Facebook MessengerGoogle Vertex AI + GithubGoogle Vertex AI + Google Cloud BigQueryGoogle Vertex AI + Google driveGoogle Vertex AI + Google sheetsGoogle Vertex AI + Google FormsGoogle Vertex AI + Gemini
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