Best Big Data Books to Read in 2018
Best big data books to read as an intern, a manager or a technical person
If you want to improve your big data knowledge, then this article will give you an overview over some of the best big data books that can bring more light to the subject. Regardless of your current job title, these books will satisfy your big data curiosity.
Where should an intern or a student start from when learing big data?
Authors: Judith Hurwitz, Alan, Fern Halper, Marcia Kaufman
Despite the name, this is actually a very useful book that makes a clear overview of what big data is. It is a helpful tool for an intern that starts working in the big data field, or even for a student, as the way it explains big data can be easily understood by the uninitiated.
However, Big Data for Dummies can also help managers by helping them take charge of big data solutions for their organization. It provides essential information in a no-nonsense and easy-to-understand way. By reading this book, you are going to understand how to leverage big data tools and architectures, explore how big data can transform any business or how to use predictive analytics. The book authors are experts in information management and big data.
Amazon Review: Highly recommend as a primer on Big Data
“A great primer and reference for Hadoop and Big Data beginners. Not a programming or configuration reference, but you will learn how traditional and modern data systems have come about, and how they are used. A great way to get up to speed on the modern jargon surrounding Big Data, plus you get enough context that you will figure out what book (if any) you need to read next for your needs.”
Author: Garry Turkington
When you’re learning about big data, it’s a matter of time until the word Hadoop appears. This guide will walk you through how to install and run Hadoop. Moreover, this book will teach you what tools and techniques let you easily approach and use big data, and it will show you how to build a complete data infrastructure. Hands-on examples are included in each chapter, offering you the big picture while also expressing the practical use.
Amazon Review: A Great practical book to start Hadoop hands on from scratch
"I am very interested in Big Data and have read many books on the subject. It was time for me to get hands. So I was looking for a beginner book for Hadoop. I already knew Big Data as a subject, and I also knew the type of problems we are solving. So I looked for a beginner book for Hadoop as I did not have any prior experience with the platform. I am using Mac OSX machine. I know my way around Linux/Unix shell."
Others titles worth exploring:
Every retail or e-commerce managers should read these big data books
Author: P. Simon
Phil Simon explains how big data is not only an area of potential innovation but also a crucial factor that companies must address to survive in the modern marketplace. His arguments contain urgency and clarity, centering on the fact that big data is not just a simple craze; it’s a huge change in how business is conducted and it’s already happening.
Filled with case studies and examples, Too Big To Ignore is a great introduction to big data, as seen through the lens of “what can big data do for me and my business?”
Amazon Review: This is a must read!
"Excellent read, - concisely makes the business case, also introduces some exciting startups working on the core technologies that comprise BigData."
Author: Thomas H. Davenport
Big Data at Work covers all the basic information that will become fundamentals for each manager that wants to be updated with the big data phenomenon. It brings an overview of what big data means from a technical, management and consumeristic perspective: what its opportunities and costs are, where it can have real business impact, and which aspects of this hot topic have been oversold. This book will also help you understand how to build your big data team in order to successfully implement any big data project.
"It’s a required reading for managers that need a straightforward, hype-free introduction to big data, a clear and clarifying “signal” in the incredible noise around the confusing and mislabeled term."
You can also read:
- Business UnIntelligence: Insight and Innovation Beyond Analytics and Big Data, by B. Devlin
- Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, by B. Baesens
Technical workers not only need to improve their practical skills but also be updated with advancements in big data
Authors: Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills
In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems.
The journey starts with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance.
"It is a well written book. I found the chapters on PySpark and MLib useful. However, the topics on genomic data and neuroimaging weren't quite consistent and probably will require more attention."
Authors: Ian H. Witten, Eibe Frank, Mark A. Hall
This book brings practical advice on applying machine learning tools and techniques to real-world data mining situations. It also offers practical tips and techniques for performance improvement that work by transforming the input or output in machine learning methods.
"Great book -One of the best books I have read on the subject thus far.
There seems to be so much hype on "data science" these days when actuaries were doing this stuff with slide rules decades ago.This book removes the mystery and explains it....An understanding of data architecture and some math would be helpful, but I think anyone with a technical background would benefit from it."
You can also read:
- Designing Data-Intensive Applications , by Martin Kleppmann
Note: The books presented above and this classification could be different, based on each person’s level of knowledge in the field. Let us know in the comments what you consider to be the best books on big data so far!