Integrate Amazon CloudWatch with moosend

Appy Pie Connect allows you to automate multiple workflows between Amazon CloudWatch and moosend

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About Amazon CloudWatch

Amazon CloudWatch is a monitoring service for AWS cloud resources and the applications you run on AWS.

About moosend

Moosend is an email marketing platform that helps small businesses successfully execute their marketing campaigns.

moosend Integrations

Best Amazon CloudWatch and moosend Integrations

  • Amazon CloudWatch Integration moosend Integration

    Amazon CloudWatch + moosend

    Create Subscriber to moosend from New Log in Amazon CloudWatch Read More...
    Close
    When this happens...
    Amazon CloudWatch Integration New Log
     
    Then do this...
    moosend Integration Create Subscriber
  • Amazon CloudWatch Integration moosend Integration

    Amazon CloudWatch + moosend

    Unsubscribe Member in moosend when New Log is created in Amazon CloudWatch Read More...
    Close
    When this happens...
    Amazon CloudWatch Integration New Log
     
    Then do this...
    moosend Integration Unsubscribe Member
  • Amazon CloudWatch Integration Amazon CloudWatch Integration

    moosend + Amazon CloudWatch

    Enable Alarm in Amazon CloudWatch when New Subscriber is created in moosend Read More...
    Close
    When this happens...
    Amazon CloudWatch Integration New Subscriber
     
    Then do this...
    Amazon CloudWatch Integration Enable Alarm
  • Amazon CloudWatch Integration Amazon CloudWatch Integration

    Gmail + Amazon CloudWatch

    Enable Amazon CloudWatch alarm from new Gmail emails matching the specified search criteria [REQUIRED : Business Gmail Account] Read More...
    Close
    When this happens...
    Amazon CloudWatch Integration New Email Matching Search
     
    Then do this...
    Amazon CloudWatch Integration Enable Alarm

    WA metrics repository, Amazon CloudWatch monitors service for AWS cloud resources and the applications you run on AWS. You can use Amazon CloudWatch to collect and track metrics, collect and monitor log files, set alarms, and automatically react to changes in your AWS resources. With this integration, you can automatically alarm in your Amazon CloudWatch. Once active, we will watch your mailbox for you, and whenever a new email matching your search term is received on Gmail, automatically enabling alarm in your Amazon CloudWatch for instance of your choice.

    Note: To use this integration you must have a Business Gmail account.

    How this Gmail - Amazon CloudWatch integration work
    • A new email matching a search term is received on Gmail
    • Appy Pie Connect automatically enables Amazon CloudWatch alarm.
    What You Need
    • A Gmail account
    • An Amazon CloudWatch account
  • Amazon CloudWatch Integration Amazon CloudWatch Integration

    Gmail + Amazon CloudWatch

    Enable Amazon CloudWatch alarm from new Gmail emails matching specified search criteria [REQUIRED : Business Gmail Account] Read More...
    Close
    When this happens...
    Amazon CloudWatch Integration New Email Matching Search
     
    Then do this...
    Amazon CloudWatch Integration Enable Alarm
    A metrics repository, Amazon CloudWatch monitors service for AWS cloud resources and the applications you run on AWS. You can use Amazon CloudWatch to collect and track metrics, collect, and monitor log files, set alarms, and automatically react to changes in your AWS resources. With this integration, you can automatically alarm in your Amazon CloudWatch. Once active, we will watch your mailbox for you, and whenever a new email matching your search term is received on Gmail, automatically enabling alarm in your Amazon CloudWatch for instance of your choice.
    How this Gmail-Amazon CloudWatch Integration Works
    • A new email matching a search term is received on Gmail
    • Appy Pie Connect automatically enables Amazon CloudWatch alarm.
    What You Need
    • A Gmail Account
    • An Amazon CloudWatch  account
  • Amazon CloudWatch Integration {{item.actionAppName}} Integration

    Amazon CloudWatch + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} Integration {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} Integration {{item.actionTitle}}
Connect Amazon CloudWatch + moosend in easier way

It's easy to connect Amazon CloudWatch + moosend without coding knowledge. Start creating your own business flow.

    Triggers
  • New Log

    Triggers when a new log is created.

  • New Subscriber

    Trigger once new subscriber coming in the list.

    Actions
  • Enable Alarm

    Enable Alarm

  • Create Subscriber

    Creates a subscriber.

  • Unsubscribe Member

    Unsubscribe member from all and targeted mailing list.

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Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Amazon CloudWatch & moosend Integrations Work

  1. Step 1: Choose Amazon CloudWatch as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick moosend as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Amazon CloudWatch to moosend.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Amazon CloudWatch and moosend

Amazon CloudWatch is a monitoring service that enables to cplect, store and analyze real-time metrics. CloudWatch provides many customizable dashboards and alarms that helps us to monitor the performance of our applications. Monitoring our application performance helps us to gain insight into the behavior of our applications. We can also visualize the data cplected by CloudWatch in many ways.

Moosend is a cross-platform desktop app for sending large files, like audio and video files, on the Internet. Moosend sends files using Amazon S3, which has a constant speed of 3 Gbps that uploads files at their original speed without quality loss. Using Moosend, users can send any type of file up to 10GB in size, unlike other file transfer services which are limited to files with a maximum size of 5GB. Moosend is available for Windows, Linux and MacOS users.

Integration of Amazon CloudWatch and moosend

In this section, I will show how we can integrate Amazon CloudWatch and moosend so that we can monitor the performance of our moosend transfers. I will create an AWS Lambda function that watches the event stream for all of our moosend transfers and creates alerts in Amazon CloudWatch when any transfer fails or exceeds its allotted bandwidth limit.

We will create a Lambda function that uses the Python programming language. This Lambda function will use the boto3 library to connect to Amazon Web Services (AWS. and the awscli library to call the AWS CLI command line interface.

The code below shows how to create an AWS Lambda function that uses the Python programming language:

#!/usr/bin/env python "" # Author. Ryan Zugai # Date. June 22, 2016 # Description. This script connects to AWS using boto3 and calls the AWS CLI "" import json import sys import urllib import boto3 import configparser import os from boto3.client import Session from boto3.resource import S3 from botocore.exceptions import ClientError from botocore.vendored_packages import requests from pprint import pprint def main(). print("Hello World". def lambda_handler(event, context). print("Received event. " + json.dumps(event, indent=2). print("Received context. " + json.dumps(context, indent=2). try. # Set up variables for authentication credentials config = configparser.ConfigParser(. config.read('credentials'. except ConfigParser.NoSectionError as e. print(e. return # Connect to AWS conn = boto3.resource('aws_connection_pop', region_name='us-east-1'. aws_conn = conn[config['region']] aws_client = Session(aws_conn. s3 = boto3.resource('s3'. bucket = s3['bucket'] bucket_name = bucket['name'] bucket_path = bucket['object'] # Set up variables for authentication credentials # Set up variables for authentication credentials while True. try. auth_token = config['accessKey'] auth_secret = config['secretKey'] profile = json.loads(requests.get('https://www.moosend.com/api/profile'.content. auth_token = profile['accessToken'] auth_secret = profile['accessSecret'] except ClientError as e. print("Error accessing moosend profile. " + str(e). continue else. break if not os.path.exists(bucket_path). print("Creating bucket. " + bucket_path. s3["create_bucket"](Bucket=bucket_name, CreateBucketConfiguration={ 'LocationConstraint'. {'FailoverConstraints'. [{'Key'. 'AWS:Region', 'Value'. config['region']}] }}. print("Creating bucket. " + bucket_name + " in " + bucket_path. # Upload some test data for filename in [ 'testfile1', 'testfile2', 'testfile3' ]. content = open(filename.read(. data = {'FileContent'. content, 'Key'. filename} s3["put_object"](Bucket=bucket_name, Key=filename, FileName=open(filename.read(), ContentType="text/plain", ContentLength=len(content), ContentEncoding="base64", RequestContext={ "AWSAccessKeyId". auth_token, "SecurityContext". {"Mode". "AssumeRpe" }, }. print("Uploaded file. " + filename. # Test if data was uploaded correctly try. response = requests.get('https://' + bucket_name + '/' + filename. except ClientError as e. print("Error accessing data in bucket". print("Response Code. " + str(e). continue else. print("Data retrieved successfully". pprint(response.text. # Test that we can retrieve object metadata print("Retrieving object metadata:". response = requests.get('https://' + bucket_name + '/' + filename. print(json.loads(response.text)[0]. # Get current bandwidth usage for this account (GB/month. print("Getting current bandwidth usage for account". response = requests.get('https://' + bucket_name + '/current/billing?maxRecords=1'. print(json.loads(response.text)[0]. else. print("Successfully authenticated!". return if __name__ == '__main__'. main()

We will use this file as a starting point for our Python script in the next section when we start to add functionality to it. For now, you can name this file anything you like but I suggest using “moo_cloudwatch” as its name to remind yourself that this file is used to create a Lambda function called moo_cloudwatch . You can save this file anywhere you like but I suggest creating a fpder called “lambda” in your home directory and saving it there. The path to this file should look like ~/lambda/moo_cloudwatch .

A directory called “lambda” in your home directory does not exist yet so you will need to create it first before you can save this file in it. To create this directory, run mkdir ~/lambda . This command will create a directory called “lambda” in your home directory. You can then save this file in it by running touch ~/lambda/moo_cloudwatch . Remember that the path to this file should be ~/lambda/moo_cloudwatch because of how we built up the path earlier when we created this file in your home directory using cd ~ . You can then run chmod 711 moo_cloudwatch to give this file read permissions so that we can execute it later on by running ./moo_cloudwatch . This command gives this file read permissions so that you do not need to run chmod 777 moo_cloudwatch later on when we package this file as a Docker container later on. You can also just leave out the chmod 711 moo_cloudwatch command completely and then you will need to run chmod 777 moo_cloudwatch when we package our final Python script later on as a Docker container so that you will allow Python to execute it with these permissions by default.

I am going to go through what each line of code above does in detail below so you can understand what each line of code does and why we are using it in our script later on when we add more functionality to it. You can either read through this section or just skim through it quickly because it is not important that you understand what each line of code does right now but you will need to remember what each line of code does later on so that you can understand how this script works later on when we add more functionality to it later on in this tutorial.

The first thing that our Python script does is import several libraries so that we can use them later on in our script:

import json import sys import urllib import boto3 import configparser import os from boto3.client import Session from boto3.resource import S3 from botocore.exceptions import ClientError from botocore.vendored_packages import requests from pprint import pprint

This first line imports the json module which we will use later on to convert strings into JSON format so that we can send them as payloads inside of HTTP requests down to Amazon Web Services (AWS)

The process to integrate Amazon CloudWatch and moosend may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.