How to connect Google Cloud BigQuery and Bitbucket
Create a New Scenario to Connect Google Cloud BigQuery and Bitbucket
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Google Cloud BigQuery, triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Cloud BigQuery or Bitbucket will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery or Bitbucket, and select the appropriate trigger to start the scenario.

Add the Google Cloud BigQuery Node
Select the Google Cloud BigQuery node from the app selection panel on the right.

Google Cloud BigQuery
Configure the Google Cloud BigQuery
Click on the Google Cloud BigQuery node to configure it. You can modify the Google Cloud BigQuery URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Bitbucket Node
Next, click the plus (+) icon on the Google Cloud BigQuery node, select Bitbucket from the list of available apps, and choose the action you need from the list of nodes within Bitbucket.

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Authenticate Bitbucket
Now, click the Bitbucket node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Bitbucket settings. Authentication allows you to use Bitbucket through Latenode.
Configure the Google Cloud BigQuery and Bitbucket Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Google Cloud BigQuery and Bitbucket Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

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Trigger on Webhook
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Webhook response

Save and Activate the Scenario
After configuring Google Cloud BigQuery, Bitbucket, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the Google Cloud BigQuery and Bitbucket integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Bitbucket (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Google Cloud BigQuery and Bitbucket
Bitbucket + Slack: When a new commit is made to a Bitbucket repository, a message is sent to a designated Slack channel to notify the team of the commit details.
Bitbucket + Jira: When a new commit is made to a Bitbucket repository, and the commit message is linked to a Jira issue, create a new comment in that Jira issue with the commit details.
Google Cloud BigQuery and Bitbucket integration alternatives
About Google Cloud BigQuery
Use Google Cloud BigQuery in Latenode to automate data warehousing tasks. Query, analyze, and transform huge datasets as part of your workflows. Schedule data imports, trigger reports, or feed insights into other apps. Automate complex analysis without code and scale your insights with Latenode’s flexible, pay-as-you-go platform.
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About Bitbucket
Automate code deployments and issue tracking by connecting Bitbucket to Latenode. Trigger workflows on commit events, automatically update project management tools, and send notifications. Latenode provides a visual editor, affordable scaling, and custom JS nodes for complex branching and data transformation. Streamline your DevOps pipeline with flexible automation.
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See how Latenode works
FAQ Google Cloud BigQuery and Bitbucket
How can I connect my Google Cloud BigQuery account to Bitbucket using Latenode?
To connect your Google Cloud BigQuery account to Bitbucket on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud BigQuery and click on "Connect".
- Authenticate your Google Cloud BigQuery and Bitbucket accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I trigger BigQuery queries on Bitbucket code commits?
Yes, you can! Latenode's visual editor makes it easy to trigger BigQuery queries whenever code is committed to Bitbucket, automating data analysis based on development changes.
What types of tasks can I perform by integrating Google Cloud BigQuery with Bitbucket?
Integrating Google Cloud BigQuery with Bitbucket allows you to perform various tasks, including:
- Automatically logging Bitbucket events into a BigQuery dataset.
- Triggering data analysis workflows upon specific code commits.
- Generating reports on code quality metrics stored in BigQuery.
- Synchronizing user access data between Bitbucket and BigQuery.
- Auditing code changes against data processing pipeline updates.
Can I use custom SQL queries in BigQuery via Latenode?
Yes! Latenode allows you to execute custom SQL queries in Google Cloud BigQuery, giving you full control over your data extraction and transformation logic.
Are there any limitations to the Google Cloud BigQuery and Bitbucket integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Complex data transformations might require JavaScript code blocks.
- Initial setup requires appropriate permissions for both services.
- Large datasets can impact workflow execution time and resources.