ODBIERZ TWÓJ BONUS :: »

Chaos Engineering with Python Mandeep Ubhi

Język publikacji: 1
Chaos Engineering with Python Mandeep Ubhi - okladka książki

Chaos Engineering with Python Mandeep Ubhi - okladka książki

Autor:
Mandeep Ubhi
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
210
Dostępne formaty:
     ePub
     Mobi

Ebook 89,91 zł najniższa cena z 30 dni

109,00 zł (-10%)
98,10 zł

Dodaj do koszyka lub Kup na prezent Kup 1-kliknięciem

89,91 zł najniższa cena z 30 dni

Przenieś na półkę

Do przechowalni

Description
Chaos Engineering with Python is a comprehensive guide to designing, executing, and automating chaos experiments to build resilient systems. The book blends foundational theory with hands-on practice, ensuring readers gain an understanding of implementing chaos engineering effectively.

It begins by defining resilience and tracing the evolution of chaos engineering from traditional testing methods. A core focus of the book is real-world application, demonstrating structured chaos experiments across various environments. Readers will learn fault injection techniques, how to analyze experiment results, and how to use tools like the Python Chaos Toolkit. The book extensively covers chaos engineering on Kubernetes, a critical skill for modern cloud-native applications, and explores experiments on virtual machines and AWS infrastructure, in addition to providing an overview of the managed chaos services. The book also emphasizes integrating chaos experiments into CI/CD pipelines, enabling automated, continuous resilience testing as part of the development workflow. Beyond tech, it provides guidance on embedding, measuring, and sustaining the cultural shift needed to embrace chaos engineering.

This book is a valuable resource for anyone looking to understand and implement chaos engineering, from beginners to experienced practitioners, providing both the technical know-how and the cultural understanding necessary for building truly resilient systems.

Key Features
Progressive learning, starting with chaos principles, fault tolerance patterns to practical use cases, and finally to integration with CI/CD.
Real-world use cases, including Kubernetes and cloud infrastructure, built with a structured and repeatable approach.
Proven methods to embed chaos engineering culture in the enterprise and highlight its business value to stakeholders.

What you will learn
Understand chaos engineering principles, resilience, and proactive failure testing.
Implement chaos experiments using Python, Kubernetes, and cloud environments.
Integrate chaos testing into CI/CD for continuous resilience validation.
Apply fault injection techniques across VMs, infrastructure, and cloud systems.
Leverage Python Chaos Toolkit for automated and structured chaos experiments.
Foster a chaos engineering culture within DevOps and SRE teams.

Who this book is for
This book is for software engineers, DevOps engineers, SREs, and IT professionals looking to implement chaos engineering. Readers should have basic programming knowledge and familiarity with cloud computing concepts, though a Python refresher is provided.

Table of Contents
1. Resilience and Evolution of Chaos Engineering
2. Rapid Refresher on Python Essentials
3. Implementation Journey of Chaos Experiments
4. Up and Running with Python Chaos Toolkit
5. Chaos Experiments on Virtual Machines
6. Chaos Experiments with Kubernetes
7. Chaos Experiments with Infrastructure
8. Integrating Chaos Experiments into CI/CD Pipelines
9. Embedding Chaos Engineering Culture

BPB Publications - inne książki

Zamknij

Przenieś na półkę
Dodano produkt na półkę
Usunięto produkt z półki
Przeniesiono produkt do archiwum
Przeniesiono produkt do biblioteki

Zamknij

Wybierz metodę płatności

Ebook
98,10 zł
Dodaj do koszyka
Sposób płatności
Zabrania się wykorzystania treści strony do celów eksploracji tekstu i danych (TDM), w tym eksploracji w celu szkolenia technologii AI i innych systemów uczenia maszynowego. It is forbidden to use the content of the site for text and data mining (TDM), including mining for training AI technologies and other machine learning systems.