Best Spark Books This Year [Learn Apache Spark ASAP]

12 Best Spark Books in 2022 [Learn Apache Spark ASAP]

What is Apache Spark?

Apache Spark is an open-source unified analytics engine for processing big data. You can use it to quickly write applications in:

You can also use it interactively from the shells of Python, R, Scala and SQL.

Spark runs standalone or with Hadoop, Apache Mesos, Kubernetes or in the cloud.

Fun Apache Spark Fact:

Spark Contributors

1200

and counting

Over 1200 developers from 300 companies have contributed to Spark since 2009.

Apache Spark vs Hadoop – What’s the difference?

Well, there are a few core differences between Spark and Hadoop. Perhaps the most noticeable is the performance.

First, Spark runs about 100 times faster than Hadoop.

That’s because Spark uses random access memory (RAM) instead of writing data to disks 💾 like Hadoop.

Second, you’ll also find that Spark tends to be more user-friendly and supports more languages than Hadoop.

Third, Hadoop tends to be easily scalable and more secure, unlike Spark.

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TLDR: Best Spark Books This Year

🔥 Best Overall 🔥
Spark: The Definitive Guide


💥 Best for Newbies 💥
Learning Spark: Lightning-Fast Data Analytics


💸 Best Value 💸

Mastering Spark with R

Best Spark Books

1. Spark: The Definitive Guide

↘️ Ideal for: Spark newbies
↘️
Topics covered: big data, Spark, debugging

big data books Spark The Definitive Guide with a swallow

We think Spark: The Definitive Guide is one of the overall best Spark books.

While it focuses on Spark 2.0, you’ll still find plenty of relevant information such as how to use, deploy and maintain Spark. Starting with a general overview, you’ll advance to learn about:

✅ Spark’s core APIs

✅ how Spark runs on a cluster

✅ debugging and monitoring Spark applications

✅ how to apply MLlib to different problems

And much more.

➡️ Spark: The Definitive Guide is one of the best Spark books because it was written by Bill Chambers and Matei Zaharia (the creator of Spark).

Spark application architecture in Spark: The Definitive Guide

🔥 Geena’s Hot Take

There’s a reason this book is called The Definitive Guide. I mean, it was written by the CREATOR of Spark.

You’re not going to get a better teacher than that.

As a first-timer, you sure as heck won’t see me messing with another Spark book.

2. Learning Spark: Lightning-Fast Data Analytics

↘️ Ideal for: Spark newbies
↘️
Topics covered: Spark operations and configurations

Learning Spark is one of the best Spark books for newbies.

You’ll learn a lot of what’s covered in Spark: The Definitive Guide, but with Spark 3.0. Plus you’ll find the foreword by Matei Zaharia, the creator of Apache Spark.

With step-by-step walkthroughs and code snippets, you’ll discover machine learning algorithms and simple and complex data analytics.

You’ll also learn about:

✅ high-level APIs

✅inspecting, tuning and debugging Spark operations

✅ building reliable data pipelines with Delta Lake

✅ developing machine learning pipelines with MLlib

And beyond.

➡️ Learning Spark shows data scientists the importance of structure and unification in Spark.

Here’s what Spark developers are saying about Learning Spark:

Nice book if you really want to work hands on without having to worry about internals of Spark.

– Amar, Spark Developer


⚙️ Light up your Spark journey with the video course Apache Spark Fundamentals on Pluralsight.

3. Mastering Spark with R

↘️ Ideal for: data scientists using R programming
↘️
Topics covered: data science, cluster computing

Mastering Spark with R is one of the best Spark books for R developers on a budget.

Covering everything from data science topics to cluster computing, you’ll learn how to analyze, explore, transform, and visualize data using Spark with R.

You’ll also:

✅ create statistical models

✅ perform analysis and modeling

✅ learn about alternative modeling frameworks

And much, much more.

Some advanced topics you’ll cover include custom transformations, real-time data processing, and creating custom Spark extensions.

➡️ Mastering Spark with R contains beginner, intermediate and advanced concepts for using Spark with R.

Diagram of the world’s capacity to store information in Mastering Spark with R

Here’s what Spark developers are saying about Mastering Spark with R:

This book serves both as a concise quick-start guide to the Spark ecosystem and a robust long-term guide.

Emily Riederer, Spark Developer

⚙️ Get your machine learning feet wet with the video course Data Engineering and Machine Learning using Spark on Coursera.

4. Spark in Action, 2nd Edition

↘️ Ideal for: Spark, Scala and Hadoop newbies
↘️
Topics covered: writing Spark apps, application architecture

Spark in Action shows you how to build end-to-end analytics applications with code snippets in Java, Python and Scala. You’ll find plenty of real-world examples including a data pipeline for processing NASA satellite data. 🚀

You’ll also learn:

✅ how to write Spark applications in Java

✅ the Spark application architecture

✅ querying distributed datasets using Spark SQL

And more.

➡️ Spark in Action teaches you how to integrate Spark using your programming language of choice.

How you can use Spark in the book Spark in Action

5. Graph Algorithms: Practical Examples in Apache Spark and Neo4j

↘️ Ideal for: experienced Spark developers
↘️
Topics covered: graph algorithms, graph analytics, algorithms

Graph Algorithms is one of the best Spark books for experienced Spark and Neo4j developers.

You’ll learn about graph algorithms and how they can reveal:

✅ bottlenecks

✅ vulnerabilities

✅ improving machine learning predictions

And beyond.

You’ll also explore when and which algorithms to use for different types of questions. And finally, you’ll create a machine learning workflow by combining Spark and Neoj4.

➡️ Graph Algorithms contains over 20 hands-on graph algorithm examples.


6. Hands-On Deep Learning with Apache Spark

↘️ Ideal for: Scala developers, data scientists, data analysts
↘️
Topics covered: deep learning basics, Apache Spark

Hands-On Deep Learning with Apache Spark will teach you how to accelerate the design and implementation of deep learning by using Apache Spark. You’ll start with the fundamentals of Spark and deep learning.

After exploring deep learning models, you’ll:

✅ learn deep learning algorithms

✅ look at textual analysis with Spark

✅ discover distribution modeling and neural networks

Throughout the book you’ll use deep learning frameworks like TensorFlow and Keras.

➡️ Hands-On Deep Learning with Apache Spark is for Scala developers, data scientists and data analysts who want to use Spark for deep learning models.


7. Machine Learning with Apache Spark Quick Start Guide

↘️ Ideal for: business analysts, data analysts, data scientists
↘️
Topics covered: machine learning, Apache Spark

Machine Learning with Apache Spark is one of the more challenging books in our list of best Spark books.

Instead of learning the fundamentals of Spark, you’ll learn how to use Spark with:

✅ machine learning

✅ deep learning

✅ neural networks

natural language processing

This includes learning how to deploy and configure a local development environment, how to design supervised and unsupervised learning models and beyond.

➡️ With Machine Learning with Apache Spark Quick Start Guide, you’ll discover how Spark fits into the big data ecosystem.

Vertical and horizontal scaling in Machine Learning with Apache Spark Quick Start Guide

8. Stream Processing with Apache Spark

↘️ Ideal for: experienced Spark developers
↘️
Topics covered: stream processing fundamentals

Stream Processing with Apache Spark teaches you all about integrating Spark into stream processing.

First you’ll learn essential stream processing concepts and streaming architectures. Then you’ll:

✅ create and operate streaming applications with Spark Streaming

✅ discover advanced Spark Streaming techniques

✅ pit Spark against other stream processing projects like Flink and Storm

And much more.

➡️ Stream Processing with Apache Spark aims to help you master structured streaming.


9. Apache Spark: Invent the Future

↘️ Ideal for: Spark newbies and beyond
↘️
Topics covered: Spark basics, inner workings of Spark

Apache Spark: Invent the Future teaches newbies and experienced Spark developers how to use Spark for data exploration.

You’ll find ample exercises and illustrations that will help you learn about:

✅ components of Spark

✅ Spark’s data structure

✅ characteristics of RDD

✅ how to build a Spark program

✅ custom Spark configurations

And much, much more.

➡️ Apache Spark: Invent the Future is a thorough guide for learning Spark fundamentals alongside parallel technologies.


10. Data Analytics with Spark Using Python

↘️ Ideal for: beginner to advanced Spark developers
↘️
Topics covered: integrating Spark into big data

Data Analytics with Spark Using Python takes a hands-on approach to teach you Spark’s role in big data.

Packed with real-world examples, you’ll learn about:

✅ how to create Spark clusters

✅ optimizing Spark routines

✅ integrating Spark with SQL and nonrelational datastores

✅ perform stream processing

✅ implement predictive modeling

And beyond.

➡️ Data Analytics with Spark Using Python shows you how to solve data analytics problems with Spark, PySpark and other tools.


11. Big Data Processing with Apache Spark

↘️ Ideal for: Spark newbies, experienced Python developers
↘️
Topics covered: data stream consumption, common Spark operations, AWS

big data processing with apache spark book cover

You’ll start Big Data Processing with Apache Spark by learning data processing fundamentals using RDDs, SQL and beyond. Then you’ll:

✅ write your own Python programs that interact with Spark

✅ implement data stream consumption

✅ integrate Spark with Amazon Web Services (AWS)

✅ apply data streams to Spark machine learning APIs

And much more.

➡️ Big Data Processing with Apache Spark is for software engineers who want to explore distributed systems and big data analytics.


12. Apache Spark Quick Start Guide

↘️ Ideal for: experienced Scala, Python or Java developers
↘️
Topics covered: Spark, big data

Apache Spark Quick Start Guide is one of the best Spark books for writing big data applications.

It’s an all-in-one guide to get you up and running with writing big data apps. After exploring the lifecycle of Spark applications, you’ll learn about:

✅ debugging slow applications

✅ Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis

✅ the execution flow of a Spark application

And beyond.

➡️ With Apache Spark Quick Start Guide you’ll learn how to write efficient big data applications using Spark.


Best Spark Books: Conclusion

Today we showed you the best Spark books including:

🔥 Best Overall 🔥
Spark: The Definitive Guide


💥 Best for Newbies 💥
Learning Spark: Lightning-Fast Data Analytics


💸 Best Value 💸

Mastering Spark with R

So whether you need to start from the ground up or have some Spark experience, we think these are the best Spark books around.


Spark developers are also reading:


  1. What is Apache Spark?

    Apache Spark is an open-source unified analytics engine for processing big data. You can use it to quickly write applications in Python, Java, Scala, R and SQL. You can also use it interactively from the shells of Python, R, Scala and SQL. Spark runs standalone or with Hadoop, Apache Mesos, Kubernetes or in the cloud. To learn more about Apache Spark, be sure to check out today's article where we look at 12 of the best Spark books available.

  2. What are the best Spark books?

    We think the best Spark books include Spark: The Definitive Guide by Bill Chambers and Matei Zaharia. For newbies, we liked Learning Spark: Lightning-Fast Data Analytics by Jules S. Damji, et. al. And for value, we chose Mastering Spark with R by Javier Luraschi, Kevin Kuo and Edgar Ruiz. You can learn about these books and more in today's article on best Spark books.

  3. Apache Spark vs Hadoop – What's the difference?

    Well, there are a few core differences between Spark and Hadoop. Perhaps the most noticeable is the performance. Spark runs about 100 times faster than Hadoop. That's because Spark uses random access memory (RAM) instead of writing data to disks like Hadoop. You'll also find that Spark tends to be more user-friendly and supports more languages than Hadoop. The other side of the coin is that unlike Spark, Hadoop tends to be easily scalable and more secure. To learn more about Spark, tune in to today's article where we look at some of the best Spark books around.

  4. Is Spark: The Definitive Guide worth it?

    Yes, we think Spark: The Definitive Guide is worth it. While it focuses on Spark 2.0, you'll still find plenty of relevant information such as how to use, deploy and maintain Spark. Starting with a general overview, you'll advance to learn about Spark's core APIs, how Spark runs on a cluster, debugging and monitoring Spark applications, how to apply MLlib to different problems, and much more. You can learn more about Spark: The Definitive Guide and other Spark books in today's article.

  5. Is the Learning Spark book worth it?

    Yes, we think Learning Spark is worth it. You'll learn a lot of what's covered in Spark: The Definitive Guide, but with Spark 3.0. Plus you'll find the foreword by Matei Zaharia, the creator of Apache Spark. With step-by-step walkthroughs and code snippets, you'll discover machine learning algorithms and simple and complex data analytics. You'll also learn about high-level APIs, inspecting, tuning and debugging Spark operations, building reliable data pipelines with Delta Lake, develop machine learning pipelines with MLlib, and beyond. You can learn more about Learning Spark and other Spark books in today's post.

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