Amazon SES is a cloud-based email service provider that can integrate into any application for high volume email automation
Create a HTML or a plain text email template. See the docs
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Send an email using Amazon SES. Supports simple email messaging. See the docs
Send an email replacing the template tags with values using Amazon SES. See the docs
Amazon Simple Email Service (SES) is a powerful cloud-based email sending service designed to help digital marketers and application developers send marketing, notification, and transactional emails. With the SES API, you can reliably send emails at scale, manage sender reputations, view email sending statistics, and maintain a high deliverability rate. Leveraging Pipedream's capabilities, you can integrate SES seamlessly into serverless workflows, automate email responses based on triggers from other apps, and analyze the effectiveness of your email campaigns by connecting to data analytics platforms.
module.exports = defineComponent({
props: {
amazon_ses: {
type: "app",
app: "amazon_ses",
}
},
async run({steps, $}) {
const AWS = require("aws-sdk")
const { accessKeyId, secretAccessKey } = this.amazon_ses.$auth
const ses = new AWS.SES({
accessKeyId,
secretAccessKey,
region: 'us-east-1',
})
const sesParams = {
Destination: {
ToAddresses: ["<your email here>"],
},
Message: {
Body: {
Html: {
Charset: "UTF-8",
Data: "<h1>This is a test</h1>",
},
Text: {
Charset: "UTF-8",
Data: "This is a test",
}
},
Subject: {
Charset: "UTF-8",
Data: "Test email",
}
},
Source: "<your from address here",
};
this.resp = await ses.sendEmail(sesParams).promise()
},
})
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt
file or running pip
.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}