Book Image

DevOps for Databases

By : David Jambor
Book Image

DevOps for Databases

By: David Jambor

Overview of this book

In today's rapidly evolving world of DevOps, traditional silos are a thing of the past. Database administrators are no longer the only experts; site reliability engineers (SREs) and DevOps engineers are database experts as well. This blurring of the lines has led to increased responsibilities, making members of high-performing DevOps teams responsible for end-to-end ownership. This book helps you master DevOps for databases, making it a must-have resource for achieving success in the ever-changing world of DevOps. You’ll begin by exploring real-world examples of DevOps implementation and its significance in modern data-persistent technologies, before progressing into the various types of database technologies and recognizing their strengths, weaknesses, and commonalities. As you advance, the chapters will teach you about design, implementation, testing, and operations using practical examples, as well as common design patterns, combining them with tooling, technology, and strategies for different types of data-persistent technologies. You’ll also learn how to create complex end-to-end implementation, deployment, and cloud infrastructure strategies defined as code. By the end of this book, you’ll be equipped with the knowledge and tools to design, build, and operate complex systems efficiently.
Table of Contents (24 chapters)
1
Part 1: Database DevOps
5
Part 2: Persisting Data in the Cloud
7
Chapter 5: RDBMS with DevOps
10
Part 3: The Right Tool for the Job
14
Part 4: Build and Operate
19
Part 5: The Future of Data

Summary

In summary, AI, ML, and big data are technologies that have revolutionized the way we work with data and automation. They offer a wide range of benefits to organizations, such as improved efficiency, accuracy, and decision-making. However, integrating and managing these technologies can be challenging, particularly for DevOps and engineering teams who are responsible for building, deploying, and maintaining these solutions.

One of the most significant challenges that DevOps engineers face when working with AI, ML, and big data is managing the infrastructure required to support these technologies. For example, building and maintaining cloud-based resources such as virtual machines, databases, and storage solutions can be complex and time-consuming. Infrastructure-as-code tools such as AWS CloudFormation and Terraform can help automate the process of setting up and managing cloud resources. Using these tools, DevOps engineers can easily create, update, and delete resources...