How to connect Google Cloud BigQuery (REST) and Github
Create a New Scenario to Connect Google Cloud BigQuery (REST) and Github
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 (REST), triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Cloud BigQuery (REST) or Github will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery (REST) or Github, and select the appropriate trigger to start the scenario.

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

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

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Authenticate Github
Now, click the Github node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Github settings. Authentication allows you to use Github through Latenode.
Configure the Google Cloud BigQuery (REST) and Github 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 (REST) and Github 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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Save and Activate the Scenario
After configuring Google Cloud BigQuery (REST), Github, 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 (REST) and Github integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) and Github (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 (REST) and Github
Google Cloud BigQuery (REST) + Slack: Track BigQuery query job execution, retrieve results, and send a Slack message with the query performance details when a new query job is executed.
Github + Google Cloud BigQuery (REST) + Jira: When new code is committed to a Github repository, the details of the commit are logged into a BigQuery table, and a corresponding task is created in Jira.
Google Cloud BigQuery (REST) and Github integration alternatives
About Google Cloud BigQuery (REST)
Automate BigQuery data workflows in Latenode. Query and analyze massive datasets directly within your automation scenarios, bypassing manual SQL. Schedule queries, transform results with JavaScript, and pipe data to other apps. Scale your data processing without complex coding or expensive per-operation fees. Perfect for reporting, analytics, and data warehousing automation.
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About Github
Automate code management with Github in Latenode. Trigger workflows on commits, pull requests, or issues. Build automated CI/CD pipelines, track code changes, and sync repo data with project management tools. Scale code-related automations easily and add custom logic with JavaScript nodes.
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See how Latenode works
FAQ Google Cloud BigQuery (REST) and Github
How can I connect my Google Cloud BigQuery (REST) account to Github using Latenode?
To connect your Google Cloud BigQuery (REST) account to Github on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud BigQuery (REST) and click on "Connect".
- Authenticate your Google Cloud BigQuery (REST) and Github accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I track Github code commits in BigQuery?
Yes, you can! Latenode allows you to automatically log Github commits to BigQuery for analysis and reporting. Scale insights with BigQuery & Latenode’s no-code automation.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with Github?
Integrating Google Cloud BigQuery (REST) with Github allows you to perform various tasks, including:
- Import Github repository data into BigQuery for analysis.
- Trigger workflows based on Github commit activity.
- Automate data backups from Github to BigQuery.
- Create reports on code contribution metrics using BigQuery data.
- Sync Github issue data with BigQuery datasets.
CanIqueryGithubdataandstore theresultsinBigQueryusingLatenode?
Yes, you can. Latenode automates this process, allowing you to analyze Github data in BigQuery using Javascript and AI code blocks for processing.
Are there any limitations to the Google Cloud BigQuery (REST) and Github integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Rate limits imposed by the Google Cloud BigQuery (REST) and Github APIs may affect performance.
- Initial data synchronization may take time depending on the size of your datasets.
- Complex data transformations might require custom JavaScript code.