Google Cloud BigQuery and Mautic Integration

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Google Cloud BigQuery + Mautic integration

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Google Cloud BigQuery

Mautic

Step 1: Choose a Trigger

Step 2: Choose an Action

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How to connect Google Cloud BigQuery and Mautic

Create a New Scenario to Connect Google Cloud BigQuery and Mautic

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 Mautic will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery or Mautic, 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.

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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.

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Run node once

Add the Mautic Node

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

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Authenticate Mautic

Now, click the Mautic node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Mautic settings. Authentication allows you to use Mautic through Latenode.

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Configure the Google Cloud BigQuery and Mautic Nodes

Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.

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Run node once

Set Up the Google Cloud BigQuery and Mautic 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, Mautic, 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 Mautic integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Mautic (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 Mautic

Mautic + Google Sheets + Slack: When a contact's points change in Mautic, their data is updated in a Google Sheet. Then, a notification is sent to a Slack channel.

Mautic + Google Cloud BigQuery + Slack: When a contact reaches a new score in Mautic, relevant contact information is added to Google Cloud BigQuery, and a message is sent to a Slack channel to notify the sales team.

Google Cloud BigQuery and Mautic 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.

About Mautic

Automate marketing with Mautic in Latenode. Sync contacts, track campaigns, and trigger actions based on customer behavior. Integrate Mautic with other apps for a 360° view of your customers. Use Latenode's visual editor and code blocks to build custom workflows that adapt to your business needs without complex coding or expensive add-ons.

See how Latenode works

FAQ Google Cloud BigQuery and Mautic

How can I connect my Google Cloud BigQuery account to Mautic using Latenode?

To connect your Google Cloud BigQuery account to Mautic 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 Mautic accounts by providing the necessary permissions.
  • Once connected, you can create workflows using both apps.

Can I automatically update Mautic contacts based on BigQuery data?

Yes, using Latenode, you can automate contact updates. Latenode's data transformation tools and custom JavaScript blocks ensure accurate and efficient data mapping between Google Cloud BigQuery and Mautic.

What types of tasks can I perform by integrating Google Cloud BigQuery with Mautic?

Integrating Google Cloud BigQuery with Mautic allows you to perform various tasks, including:

  • Sync BigQuery customer segments to Mautic contact lists automatically.
  • Update Mautic contact custom fields using BigQuery data analysis.
  • Trigger Mautic campaigns based on BigQuery data thresholds.
  • Enrich Mautic contact profiles with insights derived from BigQuery.
  • Automate reporting on campaign performance using BigQuery data.

How secure is data transferred between Google Cloud BigQuery and Mautic?

Latenode uses secure, encrypted connections. Data transformation occurs within Latenode; credentials are never exposed, ensuring data safety.

Are there any limitations to the Google Cloud BigQuery and Mautic integration on Latenode?

While the integration is powerful, there are certain limitations to be aware of:

  • Initial data sync might require careful mapping of fields.
  • Extremely large datasets may require optimization for performance.
  • Real-time data updates depend on the chosen trigger frequency.

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