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Unleash DevOps excellence with Python and its ecosystem of tools for seamless orchestration on both local and cloud platforms, such as GCP, AWS, and Azure Key Features Integrate Python into DevOps for streamlined workflows, task automation, and improved collaboration Combine the principles of Python and DevOps into a unified approach for problem solving Learn about Python's role in Infrastructure as Code (IaC), MLOps, networking, and other domains Book Description Python stands out as a powerhouse in DevOps, boasting unparalleled libraries and support, which makes it the preferred programming language for problem solvers worldwide. This book will help you understand the true flexibility of Python, demonstrating how it can be integrated into incredibly useful DevOps workflows and workloads, through practical examples. You'll start by understanding the symbiotic relation between Python and DevOps philosophies and then explore the applications of Python for provisioning and manipulating VMs and other cloud resources to facilitate DevOps activities. With illustrated examples, you'll become familiar with automating DevOps tasks and learn where and how Python can be used to enhance CI/CD pipelines. Further, the book highlights Python's role in the Infrastructure as Code (IaC) process development, including its connections with tools like Ansible, SaltStack, and Terraform. The concluding chapters cover advanced concepts such as MLOps, DataOps, and Python's integration with generative AI, offering a glimpse into the areas of monitoring, logging, Kubernetes, and more. By the end of this book, you'll know how to leverage Python in your DevOps-based workloads to make your life easier and save time. What you will learn Implement DevOps practices and principles using Python Enhance your DevOps workloads with Python Create Python-based DevOps solutions to improve your workload efficiency Understand DevOps objectives and the mindset needed to achieve them Use Python to automate DevOps tasks and increase productivity Explore the concepts of DevSecOps, MLOps, DataOps, and more Use Python for containerized workloads in Docker and Kubernetes Who this book is for This book is for IT professionals venturing into DevOps, particularly programmers seeking to apply their existing programming knowledge to excel in this field. For DevOps professionals without a coding background, this book serves as a resource to enhance their understanding of development practices and communicate more effectively with developers. Solutions architects, programmers, and anyone regularly working with DevOps solutions and Python will also benefit from this hands-on guide. Hands-On Python for DevOps Contributors About the author About the reviewers Preface Who this book is for What this book covers To get the most out of this book Download the example code files Conventions used Get in touch Share Your Thoughts Download a free PDF copy of this book Part 1: Introduction to DevOps and role of Python in DevOps Chapter 1: Introducing DevOps Principles Exploring automation Automation and how it relates to the world How automation evolves from the perspective of an operations engineer Understanding logging and monitoring Logging Monitoring Alerts Incident and event response How to respond to an incident (in life and DevOps) Site reliability engineering Incident response teams Post-mortems Understanding high availability SLIs, SLOs, and SLAs RTOs and RPOs Error budgets How to automate for high availability? Delving into infrastructure as a code Pseudocode Summary Chapter 2: Talking about Python Python 101 Beautiful-ugly/explicit-implicit Simple-complex-complicated Flat-nested/sparse-dense Readability-special cases-practicality-purity-errors Ambiguity/one way/Dutch Now or never Hard-bad/easy-good Namespaces What Python offers DevOps Operating systems Containerization Microservices A couple of simple DevOps tasks in Python Automated shutdown of a server Autopull a list of Docker images Summary Chapter 3: The Simplest Ways to Start Using DevOps in Python Immediately Technical requirements Introducing API calls Exercise 1 – calling a Hugging Face Transformer API Exercise 2 – creating and releasing an API for consumption Networking Exercise 1 – using Scapy to sniff packets and visualize packet size over time Exercise 2 – generating a routing table for your device Summary Chapter 4: Provisioning Resources Technical requirements Python SDKs (and why everyone uses them) Creating an AWS EC2 instance with Python’s boto3 library Scaling and autoscaling Manual scaling with Python Autoscaling with Python based on a trigger Containers and where Python fits in with containers Simplifying Docker administration with Python Managing Kubernetes with Python Summary Part 2: Sample Implementations of Python in DevOps Chapter 5: Manipulating Resources Technical requirements Event-based resource adjustment Edge location-based resource sharing Testing features on a subset of users Analyzing data Analysis of live data Analysis of historical data Refactoring legacy applications Optimize Refactor Restart Summary Chapter 6: Security and DevSecOps with Python Technical requirements Securing API keys and passwords Store environment variables Extract and obfuscate PII Validating and verifying container images with Binary Authorization Incident monitoring and response Running runbooks Pattern analysis of monitored logs Summary Chapter 7: Automating Tasks Automating server maintenance and patching Sample 1: Running fleet maintenance on multiple instance fleets at once Sample 2: Centralizing OS patching for critical updates Automating container creation Sample 1: Creating containers based on a list of requirements Sample 2: Spinning up Kubernetes clusters Automated launching of playbooks based on parameters Summary Chapter 8: Understanding Event-Driven Architecture Technical requirements Introducing Pub/Sub and employing Kafka with Python using the confluent-kafka library Understanding the importance of events and consequences Exploring loosely coupled architecture Killing your monolith with the strangler fig Summary Chapter 9: Using Python for CI/CD Pipelines Technical requirements The origins and philosophy of CI/CD Scene 1 – continuous integration Scene 2 – continuous delivery Scene 3 – continuous deployment Python CI/CD essentials – automating a basic task Working with devs and infrastructure to deliver your product Performing rollback Summary Part 3: Let’s Go Further, Let’s Build Bigger Chapter 10: Common DevOps Use Cases in Some of the Biggest Companies in the World AWS use case – Samsung electronics Scenario Brainstorming Solution Azure Use Case – Intertech Scenario Brainstorming Solution Google Cloud use case – MLB and AFL Scenario Brainstorming Solution Summary Chapter 11: MLOps and DataOps Technical requirements How MLOps and DataOps differ from regular DevOps DataOps use case – JSON concatenation MLOps use case – overclocking a GPU Dealing with velocity, volume, and variety Volume Velocity Variety The Ops behind ChatGPT Summary Chapter 12: How Python Integrates with IaC Concepts Technical requirements Automation and customization with Python’s Salt library How Ansible works and the Python code behind it Automate the automation of IaC with Python Summary Chapter 13: The Tools to Take Your DevOps to the Next Level Technical requirements Advanced automation tools Advanced monitoring tools Advanced event response strategies Summary Index Why subscribe? Other Books You May Enjoy Packt is searching for authors like you Share Your Thoughts Download a free PDF copy of this book Unleash DevOps excellence with Python and its ecosystem of tools for seamless orchestration on both local and cloud platforms, such as GCP, AWS, and AzureKey FeaturesIntegrate Python into DevOps for streamlined workflows, task automation, and improved collaborationCombine the principles of Python and DevOps into a unified approach for problem solvingLearn about Python's role in Infrastructure as Code (IaC), MLOps, networking, and other domainsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionPython stands out as a powerhouse in DevOps, boasting unparalleled libraries and support, which makes it the preferred programming language for problem solvers worldwide. This book will help you understand the true flexibility of Python, demonstrating how it can be integrated into incredibly useful DevOps workflows and workloads, through practical examples. You'll start by understanding the symbiotic relation between Python and DevOps philosophies and then explore the applications of Python for provisioning and manipulating VMs and other cloud resources to facilitate DevOps activities. With illustrated examples, you'll become familiar with automating DevOps tasks and learn where and how Python can be used to enhance CI/CD pipelines. Further, the book highlights Python's role in the Infrastructure as Code (IaC) process development, including its connections with tools like Ansible, SaltStack, and Terraform. The concluding chapters cover advanced concepts such as MLOps, DataOps, and Python's integration with generative AI, offering a glimpse into the areas of monitoring, logging, Kubernetes, and more. By the end of this book, you'll know how to leverage Python in your DevOps-based workloads to make your life easier and save time.What you will learnImplement DevOps practices and principles using PythonEnhance your DevOps workloads with PythonCreate Python-based DevOps solutions to improve your workload efficiencyUnderstand DevOps objectives and the mindset needed to achieve themUse Python to automate DevOps tasks and increase productivityExplore the concepts of DevSecOps, MLOps, DataOps, and moreUse Python for containerized workloads in Docker and KubernetesWho this book is forThis book is for IT professionals venturing into DevOps, particularly programmers seeking to apply their existing programming knowledge to excel in this field. For DevOps professionals without a coding background, this book serves as a resource to enhance their understanding of development practices and communicate more effectively with developers. Solutions architects, programmers, and anyone regularly working with DevOps solutions and Python will also benefit from this hands-on guide.