Hands-On Python for DevOps: Leverage Python's native libraries to streamline your workflow and save time with automation

Cover....1

Title Page....2

Copyright and Credits....3

Contributors....5

Table of Contents....8

Preface....14

Part 1: Introduction to DevOps and role of Python in DevOps....20

Chapter 1: Introducing DevOps Principles....22

Exploring automation....23

Automation and how it relates to the world....23

How automation evolves from the perspective of an operations engineer....23

Understanding logging and monitoring....25

Logging....25

Monitoring....26

Alerts....26

Incident and event response....26

How to respond to an incident (in life and DevOps)....27

Site reliability engineering....28

Incident response teams....29

Post-mortems....30

Understanding high availability....30

SLIs, SLOs, and SLAs....31

RTOs and RPOs....32

Error budgets....33

How to automate for high availability?....34

Delving into infrastructure as a code....34

Pseudocode....35

Summary....36

Chapter 2: Talking about Python....38

Python 101....39

Beautiful-ugly/explicit-implicit....41

Simple-complex-complicated....42

Flat-nested/sparse-dense....42

Readability-special cases-practicality-purity-errors....43

Ambiguity/one way/Dutch....43

Now or never....44

Hard-bad/easy-good....45

Namespaces....45

What Python offers DevOps....46

Operating systems....46

Containerization....47

Microservices....47

A couple of simple DevOps tasks in Python....48

Automated shutdown of a server....48

Autopull a list of Docker images....55

Summary....56

Chapter 3: The Simplest Ways to Start Using DevOps in Python Immediately....58

Technical requirements....59

Introducing API calls....59

Exercise 1 – calling a Hugging Face Transformer API....60

Exercise 2 – creating and releasing an API for consumption....63

Networking....66

Exercise 1 – using Scapy to sniff packets and visualize packet size over time....66

Exercise 2 – generating a routing table for your device....69

Summary....71

Chapter 4: Provisioning Resources....72

Technical requirements....73

Python SDKs (and why everyone uses them)....73

Creating an AWS EC2 instance with Python’s boto3 library....74

Scaling and autoscaling....76

Manual scaling with Python....77

Autoscaling with Python based on a trigger....78

Containers and where Python fits in with containers....80

Simplifying Docker administration with Python....81

Managing Kubernetes with Python....82

Summary....83

Part 2: Sample Implementations of Python in DevOps....84

Chapter 5: Manipulating Resources....86

Technical requirements....87

Event-based resource adjustment....87

Edge location-based resource sharing....88

Testing features on a subset of users....89

Analyzing data....91

Analysis of live data....91

Analysis of historical data....92

Refactoring legacy applications....93

Optimize....94

Refactor....95

Restart....96

Summary....96

Chapter 6: Security and DevSecOps with Python....98

Technical requirements....99

Securing API keys and passwords....99

Store environment variables....100

Extract and obfuscate PII....101

Validating and verifying container images with Binary Authorization....103

Incident monitoring and response....105

Running runbooks....105

Pattern analysis of monitored logs....111

Summary....115

Chapter 7: Automating Tasks....116

Automating server maintenance and patching....117

Sample 1: Running fleet maintenance on multiple instance fleets at once....118

Sample 2: Centralizing OS patching for critical updates....120

Automating container creation....121

Sample 1: Creating containers based on a list of requirements....121

Sample 2: Spinning up Kubernetes clusters....123

Automated launching of playbooks based on parameters....124

Summary....128

Chapter 8: Understanding Event-Driven Architecture....130

Technical requirements....131

Introducing Pub/Sub and employing Kafka with Python using the confluent-kafka library....131

Understanding the importance of events and consequences....133

Exploring loosely coupled architecture....136

Killing your monolith with the strangler fig....138

Summary....141

Chapter 9: Using Python for CI/CD Pipelines....142

Technical requirements....143

The origins and philosophy of CI/CD....143

Scene 1 – continuous integration....143

Scene 2 – continuous delivery....144

Scene 3 – continuous deployment....146

Python CI/CD essentials – automating a basic task....147

Working with devs and infrastructure to deliver your product....150

Performing rollback....153

Summary....155

Part 3: Let’s Go Further, Let’s Build Bigger....156

Chapter 10: Common DevOps Use Cases in Some of the Biggest Companies in the World....158

AWS use case – Samsung electronics....159

Scenario....160

Brainstorming....160

Solution....161

Azure Use Case – Intertech....162

Scenario....162

Brainstorming....163

Solution....164

Google Cloud use case – MLB and AFL....165

Scenario....167

Brainstorming....167

Solution....168

Summary....170

Chapter 11: MLOps and DataOps....172

Technical requirements....173

How MLOps and DataOps differ from regular DevOps....173

DataOps use case – JSON concatenation....173

MLOps use case – overclocking a GPU....174

Dealing with velocity, volume, and variety....175

Volume....175

Velocity....177

Variety....179

The Ops behind ChatGPT....181

Summary....182

Chapter 12: How Python Integrates with IaC Concepts....184

Technical requirements....185

Automation and customization with Python’s Salt library....185

How Ansible works and the Python code behind it....189

Automate the automation of IaC with Python....193

Summary....194

Chapter 13: The Tools to Take Your DevOps to the Next Level....196

Technical requirements....197

Advanced automation tools....197

Advanced monitoring tools....201

Advanced event response strategies....206

Summary....208

Index....210

Other Books You May Enjoy....217

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.