Validating Python code with a CodeCatalyst pipeline

CodeCatalyst is a unified development environment created by AWS.

It has many features such as blueprints to assist in writing code, integrated Git repositories, dev environments which can be pre-defined and now AI integration. However, for myself, one of the most useful things is being able to define and use pipelines stored in the code repository.

Pipelines are one of the most commonly used tools that many of us working with code and Cloud use, allowing us to automate tasks to be carried out when we make changes to our code, whether that’s checking that our code works, building artefacts and packages, and deploying to our environments.

Deploying a Hugo Blog with Amazon CodeCatalyst

CI/CD pipelines are an integral part of any developer’s toolset these days. They monitor code and when changes occur, automatically carry out a pre-defined set of actions, typically creating a build environment, and maybe running some tests before deploying the code.

Several tools can host these pipelines, and an important part of the work I do with GlobalLogic UK&I is being able to advise clients on which solutions might be best suited to their needs, and how to implement them.