This is perfect food for a Lambda which can in a later stage be used as Custom Resource within your CDK application. To make use of this load balancer we need to have the ip addresses of the VPC Endpoints created by MWAA inside your VPC. The problem with running MWAA in private mode is that when MWAA is deployed, you can not connect to the Apache Airflow UI.Īs a solution AWS describes the usage of a load balancer in front of your MWAA, see this documentation. One of the best practices is to run MWAA in a private network mode, so in a private subnet within your VPC. This is a managed orchestration service for Apache Airflow that makes it easier to set up and operate end-to-end data pipelines. So I was exploring the service called Managed Workflow Apache Airflow (MWAA). PrerequisiteĪWS access via access key and secret key as this example uses boto3 call to AWS.įirst we need to install the package via pip:īasically via the command line on your machine you can just invoke the function with for example a test event. This package lets you test your Lambda python function locally as it would be running inside AWS, so you can create a function called lambda_handler with event and context as input. Package python-lambda-localĪs a solution I use the pip package “python-lambda-local”. So normally you would create the Lambda function, deploy it via CDK, invoke the Lambda function with a test, only to see the results and find out that the function isn't working as expected. We all have been there and said, 'it works on my machine'. But often I find myself writing AWS Lambda code in my code editor and having trouble to test if the function does what it needs to do.

With CDK it is easy to package this Lambda and let CDK deploy it within your AWS environment. When working with Infrastructure as Code, for example Cloud Development Kit (CDK), it is possible to create AWS Lambda functions inside your CDK application.

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