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

Schema management

Let’s review together three unique challenges around schema management that are specific to non-relational databases.

Schemaless data modeling

One of the main characteristics of non-relational databases is that they offer a schemaless data modeling approach. This means that they don’t enforce a fixed schema on the data and allow for flexible and dynamic data structures. While this can provide many benefits, such as faster iteration and easier scalability, it can also present some challenges in schema management.

In a schemaless database, there may not be a standard way to define or enforce the structure of data. This can make it difficult to ensure data consistency and quality across different documents. Additionally, it can be challenging to maintain compatibility and manage schema changes over time.

For example, in a document-oriented database such as Couchbase, data can be stored in JSON documents with any arbitrary structure. Here’...