Hands-On Python for DevOps Leverage Python's Native Libraries to Streamline Your Workflow and Save Time with Automation
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 an...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009807525406719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- 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
- Other Books You May Enjoy.
