Big Data Books

Best Books to Read About Big Data

Big data books teach us about the role of big data in advancement in business, politics, health, education, and innovation. Big data refers to data sets too large to handle by the conservative data processing application. It entails capturing, storing, analyzing, searching, sharing, transferring, visualizing, querying, updating, info privacy, and data source. Big data was initially concerned with size, diversity, and velocity, but now the fourth dimension is veracity or insightfulness and quality of data. It has made Amazon, Netflix, and Apple successful in tapping and recommending customers.

The list of Big Data Books will help Informational Technology readers to understand and become proficient in big data. These books benefit individuals pursuing a career in Big Data.

The below book list guides beginners and professionals to progress in their careers.

#	Books	Author	Published Year	Ratings
1	Big Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking	Subhashini Chellappan Seema Acharya	2019	Amazon: 4.4 Goodreads: 4.06
2	Big Data Analytics: Introduction to Hadoop, Spark, and Machine-Learning	Raj Kamal, Preeti Saxena	2019	Amazon: 4.3 Goodreads: 4.82
3	Too Big to Ignore: The Business Case for Big Data	Phil Simon	2015	Amazon: 3.9 Goodreads: 3.66
4	Big Data for Small Business for Dummies	Bernard Marr	2016	Amazon: 4.1 Goodreads: 3.50
5	Big Data: A Very Short Introduction	Dawn E. Holmes	2017	Amazon: 4.4 Goodreads: 3.64
6	Big Data: The Essential Guide to Work, Life, and Learning in the Age of Insight	Viktor Mayer-Schonberger, Kenneth Cukier	2013	Amazon: 4.2 Goodreads: 3.69
7	Big Data in How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results	Bernard Marr	2016	Amazon: 4.6 Goodreads: 3.44
8	R for Data Science: Import, Tidy, Transform, Visualize, and Model Data	Hadley Wickham, Garrett Grolemund	2017	Amazon 4.7 Goodreads 4.57
9	Data Science and Big Data Analytics	EMC Education Service	2015	Amazon: 4.3 Goodreads: 3.88
10	Analytics in a Big Data World	Bart Baesens	2014	Amazon: 4.2 Goodreads: 3.56

Let us review the Big Data Books and see which one best serves your purpose.

Book #1 Big Data and Analytics

Authors: Subhashini Chellappan, Seema Acharya

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Review:

This book is a must-have for beginners who want a thorough understanding of Big Data. It covers every aspect of Big Data with great detail and how to extract data for business value. Data Science for Business will empower the reader with the knowledge of different Big Data Concepts, Analytics, and Hadoop. Every chapter comes with several practice sections to test the users’ skills. Foster Provost and Tom Fawcett have written for business owners, data analysts, and enthusiasts.

Key Points:

• A business owner will be able to understand how data science can serve the company and how to get the best competitive advantage.
• The book covers different segments of Big Data in detail and explains Hadoop with step-by-step examples.
• Data Analysts can solve business problems with data mining by collecting appropriate data.

Book #2 Big Data Analytics: Introduction to Hadoop, Spark, and Machine-Learning

Authors: Raj Kamal, Preeti Saxena

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Review:

Big Data Analytics is an appropriate read for beginners and covers everything from scratch, including numerous sample codes, real-life analytics, and case studies. It gradually takes the reader to the next level at Predictive-analytics and Mahout. The supportive illustrations in the book assist the readers in grasping complicated concepts effortlessly. Since the book elevates a reader from the basics to the expert level, beginners and intermediates will find Big Data Analytics helpful.

Key Points:

• The author covers the latest concepts and topics on machine learning and predictive analysis.
• They delve into Big Data NoSQL Column-family, Spark ecosystem tools, and Python.
• The book features plenty of examples and source codes with step-by-step explanations.

Book #3 Too Big to Ignore: The Business Case for Big Data

Author: Phil Simon

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Review:

The author has written the book for established and start-up business owners and technical teams. The book is filled with pointers for analysts and entrepreneurs as it delves into how big companies and organizations can use Big Data for growth. It exemplifies Amazon reaping the benefit of providing timely and appropriate recommendations to its customers employing Big Data. Additionally, it explores more such opportunities and ideas for entrepreneurs and analysts with the success of several companies.

Key points:

• The reader learns how big companies can use Big Data to their benefit.
• Companies today can grow with Big Data by making irretrievable observations and actions.
• Readers know how big data allows business owners and analysts to predict customer behaviour.

Book #4 Big Data for Small Business for Dummies

Author: Bernard Marr

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Review:

Those seeking interest in Big Data can find this book helpful. This book by Bernard Marr guides candidates through the path of Big Data using case studies, examples, and step-by-step programming codes. Upon finishing this book, the users can improve their decision-making skills in business and develop better business strategies.

Key Points:

• The book language is easy to understand for beginners as it tackles the complexity of the subject effortlessly.
• The content is perfect for beginners to develop a strong base in building business infrastructure with Big Data.
• The book offers different ideas that can help in better business decision-making.

Book #5 Big Data: A Very Short Introduction

Author: Dawn E. Holmes

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Review:

The author has kept the beginner in mind while introducing big data concepts, including data storage and analysis. Dawn E. Holmes has presented the function of big data in business and medicine. She further examines how companies and organizations that control diseases exploit that data. Elaborating on the basic concepts of Big Data through examples and simple words is what this book does best.

Key Points:

• The reader learns how businesses pick up and use data for growth.
• The book exhibits numerous examples and definitions and is perfect for those making a career in Big Data.
• It explores data in all its being, including uploaded documents, photos, videos, social media traffic, Eshopping, and GPS data from vehicles.

Book #6 Big Data: A Revolution That Will Transform How We Live, Work, and Think

Authors: Viktor Mayer-Schonberger, Kenneth Cukier

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Review:

It is one of the best books ever written on Big Data, this book deals with every aspect of Big Data and clears the concepts with simple literature. The book discusses how big data directs business, politics, health, education, and innovation. Once you go through the pages, you will learn the best actionable steps required to manage different Big Data paradigms. The book discusses how data will affect people’s lives and how to safeguard the future. It is a must-have book for those who want to shift to Big Data as their career objective.

Key Points:

• The book explains the importance of Big Data from different perspectives.
• The author compares old methods with new techniques of Big Data and how things stand out today.
• The book is theoretically rich and concise and applies to beginners and avid readers.

Book #7 Big Data in Practice

Author: Bernard Marr

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Review:

This book is not only about concepts and theories. What makes it interesting is the real-life examples of how big data made companies like Walmart, LinkedIn, and Microsoft. Similarly, there are hundreds of questions and mock tests for the readers to practice. This book improves the reader’s practical expertise while emphasizing technical detailing.

Key Points:

• The reader learns about the successful predictive analytics that helps big companies to understand customers like Amazon, John Deere, Target, and Apple.
• The author has revealed the workings of big data analytics in medicine, hospitality, law enforcement, fashion, banking, and science evolve.
• The users can develop big data strategies with the guidance of extra reading materials after each chapter.

Book #8 R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Authors: Hadley Wickham, Garrett Grolemund

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Review:

R for Data Science accelerates the reader’s performance and goes on to explore the cycle of applied data science. The user gets hands-on practice with R, tidyverse, and RStudio. Each section-ends includes exercises to enable reader skills.

Key Points:

• The readers can use datasets by capturing genuine signals to create analysis forms efficiently and quickly.
• The user gets hands-on training on problem-solving data in R and R Markdown, combining prose, code, and result.
• The book enables the reader to examine data and generate their hypotheses.

Book #9 Data Science and Big Data Analytics

Author: EMC Education Services

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Review:

The book is beneficial for data scientists. The content primarily focuses on practical applications relevant to different industries using Big Data. It features ample explanatory notes and industry-specific examples that can help with the analytics part. Thus, it enables the reader to be part of the data analytics team and further the business to newer horizons.

Key Points:

• The book allows readers to solve data analytics jargon using a systematic approach.
• The reader finds a range of examples to help prepare for professional certification courses in Data Science.
• The reader learns to choose from tools that help in Big Data analysis.

Book #10 Analytics in a Big Data World: The Essential Guide to Data Science and in Applications

Author: Bart Baesens

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Review:

Analytics in a Big Data World is another excellent book on Big Data and Data Analytics that focuses on real-world experiences and business applications. The book features real-time examples from different streams like retailing, banking, and government sectors. It covers all the latest developments in Big Data along with strategic planning and what the future holds.

Key Points:

• The book focuses more on business applications and real-world examples than complicated theories.
• The author has painstakingly covered the advancements in Big Data and Data Analytics.
• The book is helpful for a practitioner and does not stress too much on mathematical foundations.

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