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Hands-On Software Engineering with Python

Hands-On Software Engineering with Python - Second Edition

By : Brian Allbee
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Hands-On Software Engineering with Python

Hands-On Software Engineering with Python

By: Brian Allbee

Overview of this book

Software engineering is more than coding; it’s the strategic design and continuous improvement of systems that serve real-world needs. This newly updated second edition of Hands-On Software Engineering with Python expands on its foundational approach to help you grow into a senior or staff-level engineering role. Fully revised for today’s Python ecosystem, this edition includes updated tooling, practices, and architectural patterns. You’ll explore key changes across five minor Python versions, examine new features like dataclasses and type hinting, and evaluate modern tools such as Poetry, pytest, and GitHub Actions. A new chapter introduces high-performance computing in Python, and the entire development process is enhanced with cloud-readiness in mind. You’ll follow a complete redesign and refactor of a multi-tier system from the first edition, gaining insight into how software evolves—and what it takes to do that responsibly. From system modeling and SDLC phases to data persistence, testing, and CI/CD automation, each chapter builds your engineering mindset while updating your hands-on skills. By the end of this book, you'll have mastered modern Python software engineering practices and be equipped to revise and future-proof complex systems with confidence.
Table of Contents (22 chapters)
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21
Index

CI/CD implementation options

In order to fully explore what is needed for a CI/CD implementation, it is first necessary to know what is being deployed and what the final result needs to be. The project example so far has focused on code that is intended to be deployed as a Python package that is designed to be incorporated into the back-end functionality of an API, but at this point in the story of the HMS work, there has been no discussion of the API code itself, beyond setting an expectation that it would need to be a serverless implementation of some kind, with an API resource of some type calling serverless functions to handle requests and generate responses for each endpoint therein.

All three of the most popular cloud providers provide command-line interfaces (CLIs) for working with resources in their cloud spaces, so the actual deployment processes presented in this discussion will take a CLI-based approach in order to keep the same basic paradigm throughout. One of those...

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