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 our exploration of data persistence, we delved into the principles of immutable and idempotent logic. Immutable logic ensures data remains unaltered over time, offering benefits such as auditability and scalability. In contrast, idempotent logic focuses on operations that yield consistent results even when repeated, ensuring reliability and fault tolerance. The integration of these logics into data persistence systems guarantees data integrity, consistency, and enhanced error management.

Selecting appropriate data-persisting technology hinges on specific use cases. Factors such as scalability, data structure, and query needs are pivotal. For instance, while in-memory databases may suit high-performance scenarios, relational databases might be more apt for structured data with complex queries. Compliance and security are equally paramount, making it essential to opt for technologies offering robust encryption, access control, and compliance capabilities.

Looking ahead...