Welcome faithful reader to the latest edition of Inside the Data Reading Room. Long-time readers of this blog know that Inside the Data Reading Room is a semi-regular feature of this blog with the goal of introducing and reviewing new and classic books on data technology and database systems.
So without further ado, let’s dive into the first book being highlighted today, which is titled DevOps Troubleshooting: Linux Server Best Practices by Kyle Rankin (2013, Addison-Wesley; ISBN 978-0-321-83204-7).
For those who have not yet heard the term, DevOps is a growing trend for attacking the complexity of today’s modern data center. Although the exact definition of DevOps is somewhat elusive, its intent is to bolster efficiency through improved communication, collaboration and integration between software developers and other information technology professionals. A popular definition of DevOps views it as the intersection of development (software engineering), technology operations and quality assurance (QA).
How does that tie in with Rankin’s book? Well, the book focuses on managing the availability, performance, and administration of Linux servers. And that is definitely a component of DevOps. However, it is a component that most frequently falls upon SysAdmins, not necessarily developers and QA groups. That said, the material is excellent, providing multiple troubleshooting tips and guidelines for Linux.
Each chapter is clearly and concisely labelled using the high-level question you might be asking. For example, Chapter 3 is titled “Why Won’t the System Boot?” and Chapter 9 is titled “Why is the Database so Slow?” That last one could be the title of my autobiography because I’ve been asked that so often!
If you are using Linux and are tasked with any aspect of managing the server or applications accessing the server you would do well to grab a copy of DevOps Troubleshooting: Linux Server Best Practices.
Next up we have an interesting new book titled Total Information Risk Management: Maximizing the Value of Data and Information Assets by Borek, Parlikad, Webb, and Woodall (2013, Morgan Kaufmann; ISBN 978-0-321-83204-7). This book offers up a the useful research and guidelines for practitioners of information risk management.
Data and information have an effect on every business. How you manage that data and practically identify, assess, quantify, and mitigate risks that arise from poor data quality can have a large impact on your organization’s bottom line. In this age of Big Data, analytics, and the expanding role of information in business, focusing on the risk associated with such data is a wise course of action. And this book will guide you through the development and implementation of a Total Information Risk Management program for your organization.
Along the way the authors define types of information and data assets, offer up risk assessment techniques, and discuss the tools required to effectively manage information risk. Real life case studies are used to provide examples of information risk and how to manage that risk.
In today’s information-centric world identifying and mitigating the risk of your data and information assets is fast becoming a necessity instead of a luxury. Take the time to read Total Information Risk Management and you’ll be better prepared to measure the impact of gains in data quality and information insight in your organization.
The final book we’ll be examining today is Implementing Analytics: A Blueprint for Design, Development, and Adoption by Nauman Sheikh (2013, Morgan Kaufmann; ISBN 978-0-12-401696-5). The focus of this book is on using analytics to gain insight from your data. The industry hype these days is all about Big Data, but the more interesting topic is what should you be doing with all of that data? This book answers that question.
Analytics can be a difficult topic to master. But to be able to mine your operational data for business insight and competitive advantage requires analytics capabilities. The term analytics implies data discovery and analysis that can uncover unknown meaningful patterns in your data. Applying analytics to large volumes of data and metadata can help you to see significant heretofore unknown patterns in your data… patterns that can be exploited and used to improve the business.
Sheikh does a fine job of helping to simplify a complex topic. The book is broken down into three major parts. In the first part (Concept) the author delivers definitions, building blocks, and use cases to set a base of understanding. In the second part (Design) the focus turns to the mechanics of analytics including performance variables, models, automated decisions, and monitoring/tuning analytics. And in part three (Implementation) the discussion centers on implementing analytics including requirements gathering, methodology, organization, architecture, and a discussion of Big Data and Hadoop.
If you are looking for a concise and readable treatment on how you can benefit from applying analytics to your data look no further than Implementing Analytics.
And finally, Happy Thanksgiving everybody (at least everybody in the USA)!