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Top 10 Big Data Books to Read in 2024

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24th Oct, 2022
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Top 10 Big Data Books to Read in 2024

The current internet age has unofficially mandated digital presence for every brand to establish its name or to exist among the audience as an active entity. This digital activity generates enormous data each day with consistent interactions. While the data is significant and highly required to maintain customer service at its peak, keeping up with such a large and unkempt data structure is impossible. Big Data refers to this large amount of data, its related use, and extends technologies for extracting valuable insights from it. 

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Big data and its related technology’s prominence is so immense that reports claim the market of projecting an approximate growth of USD 273.4 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 11.0% globally through the forecast period. The consistent growth and competitive market encourage more people to step into the big data market through available resources and professional courses. Hence, we have curated a list of the best big data books for beginners to help kickstart your career!

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Here are the ten best books for big data to accompany your big data journey. 

Top Big Data Books 

1. Big Data for Dummies by Judith Hurwitz, Alan Nugent, Marcia Kaufman, and Dr Fern Halper

Big Data for Dummies is a great starting point for aspirants just starting in the sector who hope to understand the commonly implemented tools. The four experts have incorporated core principles to understand big data approaches through a detailed overview.

While big data dummies or novice learners can benefit greatly from this book, people seeking advanced knowledge in big data might not find it extensively useful. Nevertheless, it is a great reference book and one of the best big data books for beginners

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2. Big Data in Practice by Bernard Marr

Instead of taking up the base details and intricacies of dig data, this big data analytics book sheds light on the practical implementation, analysis, and usage of big data within active organizations. The book offers a ground perspective on big data and emphasizes how companies use it in different spaces to reap desired results. 

The book also shares technical details of its implemented projects to offer inspiration for users’ issues. This book provides a practical perspective on big data usage making it a must for learners to read.

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3. Big Data Analytics with R by Simon Walkowiak

The Big Data Analytics book is dedicated to people aiming to work with R on big data analytics. It introduces readers to the basic data analysis and algorithm processing skills, even if they lack expertise in R. Since R programming language has significant statistical fluency, its demand in the big data industry is growing.

The book starts off its journey by defining big data and R fundamentals. However, as you proceed further, R language’s implementation to big data analysis follows an excellent learning curve for people willing to dive into the topic.

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4. Spark: The Definitive Guide by Bill Chambers and Matei Zaharia

Apache Spark is a prominent name in big data analytics known for its open-source data processing. The book captures Spark’s fundamentals and detailed working along with big data and its implementation for data management. 

It is a comprehensive guide to Spark and its participation in big data while also providing various use cases for better understanding. 

5. Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer- Schonberger

Besides reading technical books on big data and its use in diverse cases, this book explores big data’s role in the current world from a non-technical perspective. It offers insight into how big data influences business decisions and daily lives. It also discusses the potential impact of big data in future industries. This is an excellent switch from the usual technical big data books and offers equally essential insights into its usage. 

6. Designing Data-Intensive Applications by Martin Kleppmann

Martin Kelppmann’s comprehensive guide to data processing and storage narrates modern techniques to apply data management tools for informed decisions. While going through modern databases, the book covers famous popular digital services and their architecture to obtain significant points. It is dedicated to software engineers, architects and managers who enjoy coding and would like to dive deeper into strengthening their skill set. 

7. Too Big to Ignore by Phil Simon

Authored by tech expert Phil Simon, the book covers fundamentals, essential tools, concepts and related big data technology to understand its current buzz in the market. With big data stepping into nearly every industry, it is essential to know its prominence and use under the same. The book also provides insights on its potential future influences on diverse industries. 

8. Ethics of Big Data: Balancing Risk and Innovation by Kord Davis and Doug Patterson

While most big data books discuss either its technicalities or influence on the current market, this book takes a step ahead. It addresses ethical concerns related to big data and its managing techniques. It is apparent how big data works with audiences’ personal detail, though its ethical consequences are hardly considered when used in bulk. 

This book navigates data-handling techniques that align with company values and practice big data management to keep privacy and ownership issues at bay. 

9. Big Data Analysis with SAS by David Pope

Big Data Analysis with SAS enables aspiring data analysts and SAS professionals to learn more about data management and implement SAS powers for improved big data operations. The book runs through features such as predictive modelling, optimisation, forecasting, and reporting to deal with big data structures and provide easy management with the help of SAS.

10. Big Data Management by Peter Ghavami

Peter Ghavami’s Big Data Management is an excellent read for corporate big data aspirants, data analysts and engineers, aiming to leverage analytics to structure big data. It also discusses detailed policies, architectures and modern strategies to deal with big data covering topics like privacy and data security through its life cycle management.

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The course is specially designed for tech professionals and novice analysts to kick start their big data journey through a reliable program extending detailed big data courses with relevant subjects. The course includes a well-rounded curriculum, including topics like big data processing, data warehousing, PySpark, and AWS cloud to keep up with industry trends. The course is created under current industry leaders, extending a reliable option for learners to attain countless future opportunities. 

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Whether you are looking for a quick handbook to strengthen your fundamentals or hoping to advance further with complex topics, these best books for big data have something for every need. Including some of these big data books in your read list can help you break the ice with big data and better prepare for tech interviews. 


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.

Frequently Asked Questions (FAQs)

1What is big data used for?

As the name suggests, big data is an accumulated heap of structure and unstructured data obtained by organisations from multiple sources. The massive amount of data can be mined through big data analytics and used to redeem valuable insights, capable of drawing organizational success through suitable implementation.

2What are the three types of big data?

Big data is classified using three different types, which include: 1) Unstructured data- Unstructured data refers to unorganised data in its raw form which lacks any pattern or structure. Unstructured data is hard to deal with and requires ML models to mine. 2) Semi-structured data- Semi-structured data follows some patterns and is not as hard to deal with as unstructured ones. While semi-structured data can be used to reap information, it requires more precisions for accurate results. 3) Structured data- Structured data is the easiest to deal with as the database clearly follows patterns, is neatly arranged, and is easy to navigate through when searching for relevant information.

3Why is big data the future?

Personalised services are peaking through improved digitisation, and the introduction of IoT only encourages machines to redeem more and more user details. The consistent flow of data is not likely to see a curb in the future. Therefore, big data is and will continue to be relevant in the future.