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

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
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
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
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
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
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
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
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
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
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
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
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
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
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
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