How to connect Google Cloud BigQuery and Amazon SES
Create a New Scenario to Connect Google Cloud BigQuery and Amazon SES
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 Amazon SES will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery or Amazon SES, 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 Amazon SES Node
Next, click the plus (+) icon on the Google Cloud BigQuery node, select Amazon SES from the list of available apps, and choose the action you need from the list of nodes within Amazon SES.

Google Cloud BigQuery
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Amazon SES

Authenticate Amazon SES
Now, click the Amazon SES node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Amazon SES settings. Authentication allows you to use Amazon SES through Latenode.
Configure the Google Cloud BigQuery and Amazon SES 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 Amazon SES 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.

JavaScript
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AI Anthropic Claude 3
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Amazon SES
Trigger on Webhook
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Google Cloud BigQuery
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Webhook response

Save and Activate the Scenario
After configuring Google Cloud BigQuery, Amazon SES, 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 Amazon SES integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Amazon SES (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 Amazon SES
Google Cloud BigQuery + Amazon SES + Slack: Analyze data in BigQuery, then send key findings via email using Amazon SES. Finally, post a summary of the analysis to a designated Slack channel.
Amazon SES + Google Cloud BigQuery + Google Sheets: Capture email bounce notifications from Amazon SES. Log this bounce data into Google Cloud BigQuery, and then visualize this data in Google Sheets for reporting.
Google Cloud BigQuery and Amazon SES 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 Amazon SES
Automate email sending with Amazon SES in Latenode. Send transactional emails, notifications, and marketing campaigns within your automated workflows. Use Latenode's visual editor to connect SES to other apps, add conditional logic, and handle bounces – simplifying email management and scaling your communication flows without coding.
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See how Latenode works
FAQ Google Cloud BigQuery and Amazon SES
How can I connect my Google Cloud BigQuery account to Amazon SES using Latenode?
To connect your Google Cloud BigQuery account to Amazon SES 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 Amazon SES accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I send personalized emails based on BigQuery data?
Yes, you can! Latenode’s visual editor simplifies this. Extract data from Google Cloud BigQuery, then use it to personalize and send emails via Amazon SES, improving engagement.
What types of tasks can I perform by integrating Google Cloud BigQuery with Amazon SES?
Integrating Google Cloud BigQuery with Amazon SES allows you to perform various tasks, including:
- Automating email reports based on BigQuery data analysis.
- Sending targeted marketing emails using BigQuery customer segments.
- Alerting users via email when specific BigQuery data thresholds are met.
- Creating data-driven email campaigns from BigQuery insights.
- Generating and emailing personalized invoices derived from BigQuery data.
How does Latenode handle Google Cloud BigQuery data security?
Latenode employs robust encryption and secure authentication protocols to protect your Google Cloud BigQuery data both in transit and at rest.
Are there any limitations to the Google Cloud BigQuery and Amazon SES integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large datasets from Google Cloud BigQuery may impact workflow execution time.
- Amazon SES sending limits are still enforced, regardless of Latenode.
- Complex data transformations may require JavaScript knowledge.