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

Database Automation

Apart from DevOps adoption, which we covered in great depth in this book so far, there were some great advancements in the field of database automation! In this chapter, we will have a high-level overview of these, highlighting their impact on today’s industry. These are the following:

  • Self-driving databases: Database management systems (DBMs) have become more autonomous and capable of managing and tuning themselves. These self-driving databases can automate tasks such as data backup, recovery, tuning, and indexing. They can also proactively repair and prevent faults, reducing the need for human intervention.
  • Artificial intelligence and machine learning enhancements: Artificial Intelligence (AI) and Machine Learning (ML) have been incorporated into database systems to analyze query performance, predict future workloads, and optimize resource allocation accordingly. This has significantly improved the efficiency and speed of databases.
  • Automated...