python_for_devops_-_learn_ruthlessly_effective_automation_by_noah_gift_kennedy_behrman_alfredo_deza_and_grig_gheorghiu

Python for DevOps - Learn Ruthlessly Effective Automation by Noah Gift, Kennedy Behrman, Alfredo Deza, and Grig Gheorghiu

Table of Contents

Python Foundations

Operations

Cloud Foundations

Data

Case Studies

Book Summary

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform.

Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to “get stuff done” in Python? This is your guide.

  • Python foundations, including a brief introduction to the language
  • How to automate text, write command-line tools, and automate the filesystem
  • Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing
  • Cloud computing, infrastructure as code, Kubernetes, and serverless
  • Machine learning operations and data engineering from a DevOps perspective
  • Building, deploying, and operationalizing a machine learning project

What Does DevOps Mean to the Authors?

Many abstract concepts in the software industry are hard to define precisely. Cloud Computing, Agile, and Big Data are good examples of topics that can have many definitions depending on whom you talk to. Instead of strictly defining what DevOps is, let’s use some phrases that show evidence DevOps is occurring:

  • Two-way collaboration between Development and Operation teams.
  • Turnaround of Ops tasks in minutes to hours, not days to weeks.
  • Strong involvement from developers; otherwise, it’s back to Devs versus Ops.
  • Operations people need development skills—at least Bash and Python.
  • Developer people need operational skills—their responsibilities don’t end with writing the code, but with deploying the system to production and monitoring alerts.
  • Automation, automation, automation: you can’t accurately automate without Dev skills, and you can’t correctly automate without Ops skills
  • Ideally: self-service for developers, at least in terms of deploying code.
  • Can be achieved via CI/CD pipelines.
  • GitOps.
  • Bidirectional everything between Development and Operations (tooling, knowledge, etc.).
  • Constant collaboration in design, implementation, deployment—and yes, automation—can’t be successful without cooperation.
  • If it isn’t automated, it’s broken.
  • Cultural: Hierarchy < Process.
  • Microservices > Monolithic.
  • The continuous deployment system is the heart and soul of the software team.
  • There are no superheroes.
  • Continuous delivery isn’t an option; it is a mandate.

About the Authors

Noah Gift is a lecturer and consultant at UC Davis Graduate School of Management in the MSBA program. Professionally, Noah has approximately 20 years’ experience programming in Python and is a member of the Python Software Foundation. He has worked for a variety of companies in roles ranging from CTO, general manager, consulting CTO, and cloud architect. Currently, he is consulting start-ups and other companies on machine learning and cloud architecture and is doing CTO-level consulting via Noah Gift Consulting. He has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps. He is also a certified AWS Solutions Architect. Noah has an MBA from the University of California, Davis; an MS in computer information systems from California State University, Los Angeles; and a BS in nutritional science from Cal Poly, San Luis Obispo. You can find more about Noah by following him on Github (https://github.com/noahgift), visiting https://noahgift.com, or connecting with him on https://linkedin.com/in/noahgift.

Kennedy Behrman is a veteran consultant specializing in architecting and implementing cloud solutions for early-stage startups. He has both undergraduate and graduate degrees from the University of Pennsylvania, including an MS in Computer Information Technology and post-graduate work in the Computer Graphics and Game Programming program.

He is experienced in data engineering, data science, AWS solutions, and engineering management, and has acted as a technical editor on a number of python and data science-related publications. As a Data Scientist, he helped develop a proprietary growth hacking machine learning algorithm for a startup that led to the exponential growth of the platform. Afterward, he then hired and managed a Data Science team that supported this technology. Additional to that experience, he has been active in the Python language for close to 15 years including giving talks at user groups, writing articles, and serving as technical editor to many publications.

Alfredo Deza is a passionate software engineer, avid open source developer, Vim plugin author, photographer, and former Olympic athlete. He has given several lectures around the world about Open Source Software, personal development, and professional sports. He has rebuilt company infrastructure, designed shared storage, and replaced complex build systems, always in search of efficient and resilient environments. With a strong belief in testing and documentation, he continues to drive robust development practices wherever he is.

As a passionate knowledge-craving developer Alfredo can be found giving presentations in local groups about Python, file systems and storage, system administration, and professional sports.

Grig Gheorghiu has worked for the last 13 years as a programmer, research lab manager, system/network/security architect, and most recently as a software test engineer. Grig is the founder of the Southern California Python Interest Group, as well as a member of the Agile Alliance and of the xpsocal user group. He holds an MS degree in Computer Science from USC. Grig blogs fairly regularly on agile testing topics at https://agiletesting.blogspot.com.

Product Details

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Python: Python Variables, Python Data Types, Python Control Structures, Python Loops, Python Functions, Python Modules, Python Packages, Python File Handling, Python Errors and Exceptions, Python Classes and Objects, Python Inheritance, Python Polymorphism, Python Encapsulation, Python Abstraction, Python Lists, Python Dictionaries, Python Tuples, Python Sets, Python String Manipulation, Python Regular Expressions, Python Comprehensions, Python Lambda Functions, Python Map, Filter, and Reduce, Python Decorators, Python Generators, Python Context Managers, Python Concurrency with Threads, Python Asynchronous Programming, Python Multiprocessing, Python Networking, Python Database Interaction, Python Debugging, Python Testing and Unit Testing, Python Virtual Environments, Python Package Management, Python Data Analysis, Python Data Visualization, Python Web Scraping, Python Web Development with Flask/Django, Python API Interaction, Python GUI Programming, Python Game Development, Python Security and Cryptography, Python Blockchain Programming, Python Machine Learning, Python Deep Learning, Python Natural Language Processing, Python Computer Vision, Python Robotics, Python Scientific Computing, Python Data Engineering, Python Cloud Computing, Python DevOps Tools, Python Performance Optimization, Python Design Patterns, Python Type Hints, Python Version Control with Git, Python Documentation, Python Internationalization and Localization, Python Accessibility, Python Configurations and Environments, Python Continuous Integration/Continuous Deployment, Python Algorithm Design, Python Problem Solving, Python Code Readability, Python Software Architecture, Python Refactoring, Python Integration with Other Languages, Python Microservices Architecture, Python Serverless Computing, Python Big Data Analysis, Python Internet of Things (IoT), Python Geospatial Analysis, Python Quantum Computing, Python Bioinformatics, Python Ethical Hacking, Python Artificial Intelligence, Python Augmented Reality and Virtual Reality, Python Blockchain Applications, Python Chatbots, Python Voice Assistants, Python Edge Computing, Python Graph Algorithms, Python Social Network Analysis, Python Time Series Analysis, Python Image Processing, Python Audio Processing, Python Video Processing, Python 3D Programming, Python Parallel Computing, Python Event-Driven Programming, Python Reactive Programming.

Variables, Data Types, Control Structures, Loops, Functions, Modules, Packages, File Handling, Errors and Exceptions, Classes and Objects, Inheritance, Polymorphism, Encapsulation, Abstraction, Lists, Dictionaries, Tuples, Sets, String Manipulation, Regular Expressions, Comprehensions, Lambda Functions, Map, Filter, and Reduce, Decorators, Generators, Context Managers, Concurrency with Threads, Asynchronous Programming, Multiprocessing, Networking, Database Interaction, Debugging, Testing and Unit Testing, Virtual Environments, Package Management, Data Analysis, Data Visualization, Web Scraping, Web Development with Flask/Django, API Interaction, GUI Programming, Game Development, Security and Cryptography, Blockchain Programming, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotics, Scientific Computing, Data Engineering, Cloud Computing, DevOps Tools, Performance Optimization, Design Patterns, Type Hints, Version Control with Git, Documentation, Internationalization and Localization, Accessibility, Configurations and Environments, Continuous Integration/Continuous Deployment, Algorithm Design, Problem Solving, Code Readability, Software Architecture, Refactoring, Integration with Other Languages, Microservices Architecture, Serverless Computing, Big Data Analysis, Internet of Things (IoT), Geospatial Analysis, Quantum Computing, Bioinformatics, Ethical Hacking, Artificial Intelligence, Augmented Reality and Virtual Reality, Blockchain Applications, Chatbots, Voice Assistants, Edge Computing, Graph Algorithms, Social Network Analysis, Time Series Analysis, Image Processing, Audio Processing, Video Processing, 3D Programming, Parallel Computing, Event-Driven Programming, Reactive Programming.


Python Glossary, Python Fundamentals, Python Inventor: Python Language Designer: Guido van Rossum on 20 February 1991; PEPs, Python Scripting, Python Keywords, Python Built-In Data Types, Python Data Structures - Python Algorithms, Python Syntax, Python OOP - Python Design Patterns, Python Module Index, pymotw.com, Python Package Manager (pip-PyPI), Python Virtualization (Conda, Miniconda, Virtualenv, Pipenv, Poetry), Python Interpreter, CPython, Python REPL, Python IDEs (PyCharm, Jupyter Notebook), Python Development Tools, Python Linter, Pythonista-Python User, Python Uses, List of Python Software, Python Popularity, Python Compiler, Python Transpiler, Python DevOps - Python SRE, Python Data Science - Python DataOps, Python Machine Learning, Python Deep Learning, Functional Python, Python Concurrency - Python GIL - Python Async (Asyncio), Python Standard Library, Python Testing (Pytest), Python Libraries (Flask), Python Frameworks (Django), Python History, Python Bibliography, Manning Python Series, Python Official Glossary - Python Glossary, Python Topics, Python Courses, Python Research, Python GitHub, Written in Python, Python Awesome List, Python Versions. (navbar_python - see also navbar_python_libaries, navbar_python_standard_library, navbar_python_virtual_environments, navbar_numpy, navbar_datascience)

DevOps: DevOps for 20 Languages by Cloud Monk (December 2024), DevOps and SRE - DevOps and CI/CD

DevOps Culture, Continuous Integration, Continuous Deployment, Continuous Delivery, Infrastructure as Code, Configuration Management, Containerization, Microservices Architecture, Monitoring and Logging, Cloud Computing, Automation Tools, Version Control Systems, CI/CD Pipelines, Testing Automation, Security in DevOps (DevSecOps), Kubernetes, Docker, Ansible, Terraform, Puppet, Chef, Git, Jenkins, GitLab CI/CD, GitHub Actions, Prometheus, Grafana, Elastic Stack (ELK), Nagios, Selenium, Load Testing, Performance Testing, Code Quality Analysis, Artifact Repository, JFrog Artifactory, Sonatype Nexus, Scrum, Agile Methodologies, Lean IT, Site Reliability Engineering (SRE), Incident Management, Change Management, Project Management, Team Collaboration Tools, Virtualization, Network Automation, Database Management and Automation, Serverless Architecture, Cloud Service Providers, API Management, Service Mesh, Observability, Chaos Engineering, Cost Optimization in Cloud

Cloud Native DevOps - Microservices DevOps - DevOps Security - DevSecOps, DevOps by Programming Language, Functional Programming and DevOps, Concurrency and DevOps, Data Science DevOps - DataOps - Database DevOps, Machine Learning DevOps - MLOps, DevOps Bibliography, DevOps Courses, DevOps Glossary, Awesome DevOps, DevOps GitHub, DevOps Topics. (navbar_devops - see also navbar_devops_focus, navbar_devsecops, navbar_cicd, navbar_agile)


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