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

Definitions and applications of AI, ML, and big data

AI is a branch of computer science that focuses on creating intelligent machines that can perform tasks that would typically require human intelligence. AI systems can analyze data, recognize patterns, and make decisions based on that analysis. Some examples of AI applications include speech recognition, computer vision, natural language processing, robotics, and expert systems.

ML is a branch of AI that concentrates on creating algorithms that can learn from given data and enhance their efficiency as time progresses. ML algorithms can automatically identify patterns in data and use them to make predictions or decisions. Some examples of ML applications include predictive analytics, fraud detection, recommender systems, image recognition, and autonomous vehicles.

Big data refers to the large and complex sets of data that are generated by modern technology. This data is often unstructured, diverse, and difficult to process using...