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

Add the Bitly Node
Select the Bitly node from the app selection panel on the right.

Bitly
Add the Google Cloud BigQuery (REST) Node
Next, click the plus (+) icon on the Bitly node, select Google Cloud BigQuery (REST) from the list of available apps, and choose the action you need from the list of nodes within Google Cloud BigQuery (REST).

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Authenticate Google Cloud BigQuery (REST)
Now, click the Google Cloud BigQuery (REST) node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Google Cloud BigQuery (REST) settings. Authentication allows you to use Google Cloud BigQuery (REST) through Latenode.
Configure the Bitly and Google Cloud BigQuery (REST) 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 Bitly and Google Cloud BigQuery (REST) 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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Save and Activate the Scenario
After configuring Bitly, Google Cloud BigQuery (REST), 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 Bitly and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Bitly and Google Cloud BigQuery (REST) (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Bitly and Google Cloud BigQuery (REST)
Bitly + Google Cloud BigQuery (REST) + Google Sheets: When a Bitlink is created, its data, including clicks, is inserted into Google Cloud BigQuery for analysis. The analyzed data is then used to update a Google Sheet for visualization.
Bitly + Google Cloud BigQuery (REST) + Slack: Shortened links are analyzed for performance in BigQuery, and if a significant performance anomaly is detected (through a query), a Slack message is sent to a designated channel.
Bitly and Google Cloud BigQuery (REST) integration alternatives
About Bitly
Use Bitly in Latenode to automatically shorten and track links within your workflows. Share campaign URLs, monitor click rates, and optimize outreach directly from your automated processes. Combine Bitly's link management with Latenode's flexible logic and data integrations for end-to-end control, avoiding manual updates and broken flows.
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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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See how Latenode works
FAQ Bitly and Google Cloud BigQuery (REST)
How can I connect my Bitly account to Google Cloud BigQuery (REST) using Latenode?
To connect your Bitly account to Google Cloud BigQuery (REST) on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Bitly and click on "Connect".
- Authenticate your Bitly and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze shortened link click data in BigQuery?
Yes, you can! Latenode simplifies sending Bitly click data to BigQuery for analysis. Gain deeper insights into link performance, visualize trends, and optimize your campaigns with ease.
What types of tasks can I perform by integrating Bitly with Google Cloud BigQuery (REST)?
Integrating Bitly with Google Cloud BigQuery (REST) allows you to perform various tasks, including:
- Automatically back up Bitly link data to Google Cloud BigQuery for archiving.
- Create custom reports on link performance using BigQuery's data analysis tools.
- Track geographic click data from Bitly links within Google Cloud BigQuery.
- Analyze the correlation between shortened link clicks and website conversions.
- Schedule daily exports of Bitly data to Google Cloud BigQuery.
How secure is connecting Bitly to BigQuery in Latenode?
Latenode employs robust security measures, including encryption and secure authentication, ensuring safe data transfer between Bitly and Google Cloud BigQuery (REST).
Are there any limitations to the Bitly and Google Cloud BigQuery (REST) integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Rate limits on the Bitly API may affect the speed of data transfer.
- Initial setup requires familiarity with Google Cloud BigQuery (REST) data schema.
- Very large datasets may require optimized queries for efficient processing.