Python And Terraform Infrastructure As Code Standards And Practices
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Python and Terraform Infrastructure as code, standards and practices
How this book is organized: A roadmap I organized this book into three sections with 13 chapters. Part 1 introduces IaC and how you, as an individual, write it. • Chapter 1 defines IaC and its benefits and principles. The chapter explains that the book has examples in Python, run by HashiCorp Terraform, and deployed to Google Cloud Platform (GCP). I also discuss the tools and use cases you’ll encounter in your IaC journey. • Chapter 2 dives into the principle of immutability and how you can migrate existing infrastructure resources to IaC. It also covers the practices of writing clean IaC. • Chapter 3 offers a few patterns for dividing and grouping infrastructure resources into modules. Each pattern includes an example and a list of use cases. • Chapter 4 covers how to manage dependencies among infrastructure resources and modules and decouple them with dependency injection and some common patterns. Part 2 describes how to write and collaborate on IaC as a team. • Chapter 5 organizes the practices and considerations for expressing IaC in different repository structures and sharing it across your team. • Chapter 6 provides an infrastructure testing strategy. It describes each type of test and how to write them for IaC. • Chapter 7 applies continuous delivery to IaC. It covers a high-level view of branching models and how your team can use them to change infrastructure. • Chapter 8 provides techniques to build secure and compliant IaC, including testing and tagging. Part 3 covers how to manage IaC across your company. • Chapter 9 applies immutability to infrastructure changes, including an example for blue-green deployments. • Chapter 10 refactors a large body of IaC to improve its maintainability and mitigate the blast radius of failed changes to one codebase. • Chapter 11 describes reverting IaC and rolling forward changes to the system. • Chapter 12 addresses the use of IaC to manage cloud computing costs. It includes an example for cost estimation of IaC. • Chapter 13 completes the book with practices to manage and update IaC tools. You will find that many concepts build on each other throughout the book, and it may help to read the chapters in order if you have not previously practiced IaC. Otherwise, you can choose the sections that best apply to the challenges you face in your IaC practice.
Terraform: Up & Running
Terraform has become a key player in the DevOps world for defining, launching, and managing infrastructure as code (IaC) across a variety of cloud and virtualization platforms, including AWS, Google Cloud, Azure, and more. This hands-on second edition, expanded and thoroughly updated for Terraform version 0.12 and beyond, shows you the fastest way to get up and running. Gruntwork cofounder Yevgeniy (Jim) Brikman walks you through code examples that demonstrate Terraform’s simple, declarative programming language for deploying and managing infrastructure with a few commands. Veteran sysadmins, DevOps engineers, and novice developers will quickly go from Terraform basics to running a full stack that can support a massive amount of traffic and a large team of developers. Explore changes from Terraform 0.9 through 0.12, including backends, workspaces, and first-class expressions Learn how to write production-grade Terraform modules Dive into manual and automated testing for Terraform code Compare Terraform to Chef, Puppet, Ansible, CloudFormation, and Salt Stack Deploy server clusters, load balancers, and databases Use Terraform to manage the state of your infrastructure Create reusable infrastructure with Terraform modules Use advanced Terraform syntax to achieve zero-downtime deployment
Streamlining ETL: A Practical Guide to Building Pipelines with Python and SQL
Unlock the potential of data with "Streamlining ETL: A Practical Guide to Building Pipelines with Python and SQL," the definitive resource for creating high-performance ETL pipelines. This essential guide is meticulously designed for data professionals seeking to harness the data-intensive capabilities of Python and SQL. From establishing a development environment and extracting raw data to optimizing and securing data processes, this book offers comprehensive coverage of every aspect of ETL pipeline development. Whether you're a data engineer, IT professional, or a scholar in data science, this book provides step-by-step instructions, practical examples, and expert insights necessary for mastering the creation and management of robust ETL pipelines. By the end of this guide, you will possess the skills to transform disparate data into meaningful insights, ensuring your data processes are efficient, scalable, and secure. Dive into advanced topics with ease and explore best practices that will make your data workflows more productive and error-resistant. With this book, elevate your organization's data strategy and foster a data-driven culture that thrives on precision and performance. Embrace the journey to becoming an adept data professional with a solid foundation in ETL processes, equipped to handle the challenges of today's data demands.