Amazon SES and Google Cloud BigQuery (REST) Integration

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Automate email analytics: Send email data from Amazon SES to Google Cloud BigQuery (REST) for analysis. Latenode's visual editor makes it easy to create custom data pipelines with flexible JavaScript transformations and scale affordably, paying only for execution time.

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Amazon SES

Google Cloud BigQuery (REST)

Step 1: Choose a Trigger

Step 2: Choose an Action

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How to connect Amazon SES and Google Cloud BigQuery (REST)

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

Add the Amazon SES Node

Select the Amazon SES node from the app selection panel on the right.

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Configure the Amazon SES

Click on the Amazon SES node to configure it. You can modify the Amazon SES 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 Google Cloud BigQuery (REST) Node

Next, click the plus (+) icon on the Amazon SES 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.

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Configure the Amazon SES 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.

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Run node once

Set Up the Amazon SES 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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Save and Activate the Scenario

After configuring Amazon SES, 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 Amazon SES and Google Cloud BigQuery (REST) integration works as expected. Depending on your setup, data should flow between Amazon SES 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 Amazon SES and Google Cloud BigQuery (REST)

Amazon SES + Google Cloud BigQuery (REST) + Slack: This automation tracks email bounce events from Amazon SES. When a new verified identity experiences bounces, the data is logged in Google Cloud BigQuery. Subsequently, a notification is sent to a designated Slack channel to alert the admin team for immediate review.

Google Cloud BigQuery (REST) + Amazon SES + Google Sheets: Analyzes marketing campaign data in Google Cloud BigQuery. After a query job completes, a summary report is sent via email using Amazon SES, and the results are saved to a Google Sheet for further analysis and record-keeping.

Amazon SES and Google Cloud BigQuery (REST) integration alternatives

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.

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.

See how Latenode works

FAQ Amazon SES and Google Cloud BigQuery (REST)

How can I connect my Amazon SES account to Google Cloud BigQuery (REST) using Latenode?

To connect your Amazon SES account to Google Cloud BigQuery (REST) on Latenode, follow these steps:

  • Sign in to your Latenode account.
  • Navigate to the integrations section.
  • Select Amazon SES and click on "Connect".
  • Authenticate your Amazon SES and Google Cloud BigQuery (REST) accounts by providing the necessary permissions.
  • Once connected, you can create workflows using both apps.

Can I track email campaign performance?

Yes, with Latenode you can automatically log email opens, clicks, and bounces from Amazon SES into Google Cloud BigQuery (REST). This offers powerful data-driven insights with minimal coding.

What types of tasks can I perform by integrating Amazon SES with Google Cloud BigQuery (REST)?

Integrating Amazon SES with Google Cloud BigQuery (REST) allows you to perform various tasks, including:

  • Automatically logging all sent email data for detailed analysis.
  • Creating dashboards to visualize email campaign effectiveness.
  • Triggering actions based on email bounce or complaint rates.
  • Building custom reports on email engagement metrics over time.
  • Enriching customer data in BigQuery with email interaction details.

How secure is my Amazon SES data within Latenode workflows?

Latenode uses secure data encryption and access controls to protect your Amazon SES and Google Cloud BigQuery (REST) data during processing and storage.

Are there any limitations to the Amazon SES and Google Cloud BigQuery (REST) integration on Latenode?

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

  • Large data transfers may incur processing time based on BigQuery's API limits.
  • Complex data transformations might require JavaScript knowledge.
  • Real-time data synchronization depends on the frequency of workflow executions.

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