Scaling and Operation you own this product

prerequisites
basic Python • AWS services basics • basics of CDK applications · operation basics
skills learned
use AWS RDS Aurora with AWS Fargate • control permissions on the container • pass parameters and secrets to the container
Chadchapol Vittavutkarnvej
1 week · 6-8 hours per week · INTERMEDIATE
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Look inside

You and your DevOps team have successfully built an application and an automated deployment pipeline for it. But before you can launch you’ll need a solid operating plan that addresses how you’ll monitor and troubleshoot your new service in production. In this liveProject, you’ll add metrics and a CloudWatch Container Insight dashboard to your Fargate service. You’ll also enable autoscaling to adjust according to CPU usage, and you’ll structure logging to be easily searched. Lastly, you’ll use Python to set up alarms so you’ll know when something goes wrong.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Chadchapol Vittavutkarnvej
Chadchapol Vittavutkarvej has worked as a software engineer in various industries for over ten years. As a solution architect, he specializes in DevOps and serverless architectures. He has collaborated on and published several blogs, open-source projects, and examples. Chadchapol holds several AWS certifications including Solutions Architect (Professional), DevOps (Professional), Security specialty, and Machine Learning specialty

prerequisites

This liveProject is for DevOps engineers who are familiar with operational concepts and are interested in setting up logging and metrics in AWS Fargate with CDK. To begin this liveProject you’ll need to be familiar with the following:

TOOLS
  • Python
  • AWS services
TECHNIQUES
  • Basic Python programming (including using the logging library)
  • Basics of AWS services
  • Deploy CDK applications
  • Operate AWS services (particularly CloudWatch and AutoScaling)

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