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What is big data?
Big data is a field that deals with data sets that are too large to be handled by traditional data-processing application software.
Today we’re showing you 10 of the best big data books that will cover:
- big data fundamentals
- big data analytics
- ethics
And much more.
This post contains affiliate links. I may receive compensation if you buy something. Read my disclosure for more details.
TLDR: Best Big Data Books
🔥 Best Overall 🔥
Big Data: Concepts, Technology, and Architecture
💥 Best for Newbies 💥
Big Data Fundamentals: Concepts, Drivers & Techniques
💸 Best Value 💸
Big Data Processing with Apache Spark
Learn Big Data
1. Big Data: Concepts, Technology, and Architecture
🚨 Ideal for: data scientists, data engineers, database managers, business intelligence analysts
💥 Major topics: data analytics, data mining, machine learning
Big Data: Concepts, Technology and Architecture by Balamarugan Balusamy, Nandhini Abirami R, Seifedine Kadry and Amir Gandomi is geared towards data scientists, data engineers, and database managers.
You’ll learn every step of the big data life cycle. This includes:
- structured, unstructured & semi-structured data
- data storage solutions
- data mining and analytics
And much, much more.
Want to take a course on big data? Check out Introduction to Big Data and Hadoop on the interactive platform Educative.io.
Then you’ll learn big data technologies like Apache Hadoop and Apache Flume.
This book is one of the best modern big data books we could find.
2. Big Data Management: Data Governance Principles for Big Data Analytics
🚨 Ideal for: data scientists, data engineers and corporate leaders
💥 Major topics: data security, privacy, life cycle management
Big Data Management by Peter Ghavami is one of the best big data books with a focus on data analytics.
Big Data Management is ideal for data scientists, data engineers and corporate leaders.
Here you’ll examine policies, strategies and recipes to manage your big data. It covers:
- data security
- privacy
- life cycle management
And more.
3. Big Data Analytics with R
🚨 Ideal for: data scientists
💥 Major topics: cloud-based data solutions, relational and non-relational databases, machine learning
In Big Data Analytics with R (Packt) by Simon Walkowiak, you’ll learn big data industry standards. Then you’ll get an introduction into R programming.
In addition, you’ll learn about cloud-based big data solutions such as Amazon EC2 and Microsoft Azure.
You’ll also learn about other big data tools such as Apache Hadoop and Apache Spark‘s machine learning library Spark MLlib.
… I don’t think there’s a better resource to learn R for data analytics…”
– sbeltran, Customer
4. Spark: The Definitive Guide: Big Data Processing Made Simple
🚨 Ideal for: Spark enthusiasts who want an in-depth look into the framework
💥 Major topics: APIs, Spark clusters, machine learning
Spark: The Definitive Guide makes our best big data books list because it was written by Bill Chambers and Matei Zaharia – the creators of Apache Spark.
Here you’ll learn how to use, deploy and maintain Spark, with an emphasis on Spark 2.0.
💡 Spark was created at UC Berkley’s AMPLab in 2009. In 2013 it was donated to the Apache Software Foundation where it became Apache 2.0.
You’ll start with a general overview of big data and Spark. Then you’ll learn about some of Spark’s core APIs like:
- DataFrames
- SQL
- Datasets
Finally, you’ll discover MLlib for machine learning classification and recommendation.
5. Big Data Analytics with SAS
🚨 Ideal for: SAS professionals, data analysts
💥 Major topics: predictive modeling, forecasting, optimizing, reporting
SAS is a statistical software suite used for data management, analytics, and more. Big Data Analytics with SAS (Packt) by David Pope was written to help you leverage the powers of SAS to analyze and process big data.
With practical and real-world examples, you’ll discover:
- predictive modeling
- forecasting
- optimizing
- reporting
Big Data Analytics with SAS will teach you how to prepare data for analysis, perform predictive forecasting and and more.
6. Data Science and Big Data Analytics
🚨 Ideal for: data scientists
💥 Major topics: techniques, deployment, tools
Data Science and Big Data Analytics by EMC Education Services is meant to help you harness the power of data to gain new insights.
Ready to dive into PySpark? Check out the course Big Data Fundamentals with PySpark on DataCamp.
You’ll discover:
- concepts
- principles
- applications
Data Science and Big Data Analytics will also help you become a contributing member of your data science team.
One of the best entry level books on big data analytics.
– LIU Bin, Customer
7. Big Data Fundamentals: Concepts, Drivers & Techniques
🚨 Ideal for: data scientists, business managers
💥 Major topics: business motivations, big data integration
Big Data Fundamentals: Concepts, Drivers & Techniques by Thomas Erl, Wajid Khattak, and Paul Buhler is arguably one of the best big data books for data scientists and business managers.
You’ll learn about the 5 Vs of datasets in big data:
- volume
- variety
- velocity
- veracity
- value
💡 Depending on who you ask, there are anywhere from 3 to 7 Vs of big data. But three are always the same: volume, variety and velocity.
Big Data Fundamentals is packed with case studies and diagrams.
8. Big Data Processing with Apache Spark
🚨 Ideal for: software engineers, architects, IT professionals
💥 Major topics: common Spark operations, integrate Spark with AWS
Big Data Processing with Apache Spark (Packt) by Manuel Galeano is one of the best big data books for software engineers and IT professionals.
First you’ll start by learning Spark fundamentals such as DataFrames, SQL and Datasets. You’ll also explore the core concepts behind Spark such as:
- Spark Streaming
- machine learning extensions
- structured streaming
And much more.
As you progress, you’ll discover how to write Python programs that interact with Spark. You’ll also work on integrating Spark Streaming with AWS.
9. Ethics of Big Data: Balancing Risk and Innovation
🚨 Ideal for: individuals and organizations
💥 Major topics: treating data ethically, data-handling practices
Ethics of Big Data by Kord Davis is a little different than the other big data books on our list.
Rather than teach you the technical aspects of big data, you’ll learn how to ethically handle it.
This books has a heavy focus on privacy and identity.
You’ll discover techniques to review your data-handling practices and seeing if they align with the organization’s values.
Then you’ll devise plans to close discrepancies between values and practices.
Finally, you’ll learn how to maintain that balance while overcoming risks and other challenges.
10. Big Data: A Very Short Introduction
🚨 Ideal for: data scientists
💥 Major topics: big data’s necessity in today’s world
In Big Data: A Very Short Introduction by Dawn Holmes, you won’t be working with any large datasets.
Instead, you’ll learn how big data is used within businesses, government and the health industry.
Want a general overview of big data? Check out the course Big Data: The Big Picture on Pluralsight.
There are a variety of case studies that examine how data is:
- stored
- analyzed
- exploited
This includes examining data security and domestic smart devices.
Big Data Books: Conclusion
Today we looked at 10 big data books:
🔥 Best Overall 🔥
Big Data: Concepts, Technology, and Architecture
💥 Best for Newbies 💥
Big Data Fundamentals: Concepts, Drivers & Techniques
💸 Best Value 💸
Big Data Processing with Apache Spark
So whether you want a decent Apache Spark book, a good value or are just getting started with big data, we think there are big data books for just about everyone.
People interested in big data books are also reading:
- Top 11 Python Books for Data Science [Learn Data Science using Python]
- 19 Best Books for Data Structures [Learn Data Structures and Algorithms]
- 9 Best Data Science Courses for Beginners [+4 Data Science Learning Paths]
- Data Science for Non-Programmers [Educative Course Review]
- Best Data Science Interview Course This Year [Educative vs DataCamp]
What are the best big data books this year?
Overall, we think Big Data: Concepts, Technology, and Architecture takes the win. If you’re a newbie, we think Big Data Fundamentals: Concepts, Drivers & Techniques might be a good fit. And for value, we think Big Data Processing with Apache Spark packs the biggest punch. Check out our post to learn more about them.
Is the book Big Data: Concepts, Technology and Architecture worth it?
Big Data: Concepts, Technology and Architecture by Balamarugan Balusamy, Nandhini Abirami R, et al. contains big data tools, terminology and technology. It’s geared towards data scientists, data engineers, and database managers. You’ll learn every step of the big data life cycle. Then you’ll look into big data technologies like Apache Hadoop and Apache Flume. You’ll also work on big data visualization with Tableau. This will enable you to create scatter plots, histograms and graphs with your data.
Throughout Big Data, you’ll look at intriguing case studies to exhibit real-world application of concepts. We explain it in today’s post.Is the book Ethics of Big Data worth it?
Rather than teach you the technical aspects of big data, you’ll learn how to ethically handle and treat that data.
With a heavy focus on privacy and identity, you’ll learn techniques to review your data-handling practices and seeing if they align with the organization’s values. Then you’ll devise plans to close discrepancies between values and practices. Finally, you’ll learn how to maintain that balance while overcoming risks and other challenges. Read today’s post to discover more.