For companies of all sizes, big data is bigger than just a catchphrase. When people talk about "big data," they often mean the rapid expansion of all types of data, including structured data in tables in databases, unstructured data in company records and emails, and semi-structured data in system log archives and web pages. The idea is to help organizations make smarter decisions faster and strengthen their bottom line. Nowadays, analytics centers on the data lake and extracts meaning from various data types. The primary goal of Apache Spark is to support this fresh approach.

Since its small start in 2009 at U.C. Berkeley's AMPLab, Apache Spark has become one of the most important big data distributed processing frameworks worldwide. The number of Apache Spark users has grown exponentially over the years. Thousands of companies, including 80% of Fortune 500, are active users of this engine. Practicing Apache Spark is a fundamental step for individuals looking to dive into data learning. In 2024, where the sources to learn are infinite, there are 20 classical, best Apache Spark books to take guidance from and make your way in big data. 

Top Apache Spark Books of 2024

Here are the top 20 Spark books to learn Apache Spark easily.

Learning Spark: Lightning-Fast Big Data Analysis - Matei Zaharia, 2015

This is the revised edition of the original Learning Spark book. It also incorporates Spark 3.0 and explains to data scientists and engineers the importance of Spark's framework and unification. This book describes how to use machine learning algorithms and carry out basic and advanced data analytics.

Data scientists, machine learning engineers, and data engineers can benefit when scaling programs to handle large amounts of data. Using the book, one can easily: 

  • Access to multiple data sources for analytical purposes
  • Learn Spark operations and SQL engine
  • Use Delta Lake to create accurate data pipelines
  • Study, modify, and troubleshoot Spark operations

Spark: The Definitive Guide: Big Data Processing Made Simple - Matei Zaharia, 2018

The book provides system developers and data engineers with useful insights to perform their jobs, including statistical models and repetitive production applications.

Readers will understand the foundations of Spark monitoring, adjusting, and debugging. Additionally, they will study machine learning methods and applications that use Spark's extensible machine learning library, MLlib. Using the book, one can easily: 

  • Get a basic understanding of big data with Spark
  • Learn about how Spark operates within a cluster
  • Processing Data Frames and SQL

High-Performance Spark: Best Practices for Scaling and Optimizing Apache Spark - Holden Karau, 2017

This book will focus on how the new APIs for Spark SQL outperform SQL's RDD data structure in terms of efficiency. The authors of this book teach you how to optimize performance so that your Spark queries can handle bigger data sets and run more quickly while consuming fewer resources.

This book offers strategies to lower the cost of data infrastructure and developer hours, making it suited for software engineers, data engineers, developers, and system administrators dealing with large-scale data-driven applications. The book is suitable for intermediate to advanced learners. The book helps learners to:

  • Find solutions to lower the cost of your data infrastructure
  • Look into the machine learning and Spark MLlib libraries

Learning Spark: Lightning-fast Data Analytics - Denny Lee, 2020

The book provides readers with information from the Apache Spark learning objectives integrated into machine learning and subjects like spark-shell basics and optimization/tuning. The book thoroughly introduces Spark application ideas across various languages, including Python, Java, Scala, and others. 

The book walks you through breaking down your Spark application into parallel processes on a cluster and interacting with Spark's distributed components. The book will help readers to:

  • Understand SQL Engine and Spark operations
  • Using Spark UI and configurations, study, adjust, and troubleshoot Spark operations
  • Create dependable data pipelines using Spark and Delta Lake

Spark in Action: Covers Apache Spark 3 with Examples in Java, Python, and Scala - Jean-Georges Perrin, 2020

This book will teach you how to leverage Spark's core capabilities and lightning-fast processing speed for real-time computing, evaluation on-demand, and machine learning, among other applications. 

The book is suitable for individuals with a basic understanding of Spark. It is a beginner-level book. The readers will learn to:

  • Understanding deployment limitations
  • Constructing complete data pipelines, cache, and checkpoints quickly
  • Understanding the architecture of a Spark application
  • Analyzing distributed datasets with Pyspark, Spark, Spark SQL, and other tools 

Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming - Gerard Maas, 2019

This book explains how to use the in-memory framework for streaming data to developers with experience with Apache Spark. The book's authors guide you through the conceptual foundations of Apache Spark. The complete guide is divided into two components that compare and contrast the streaming APIs that Spark currently supports.

Learners can use the book to:

  • Study the basic ideas of stream processing
  • Explore various streaming architectures
  • Study Structured Streaming using real-world instances
  • Integrate Spark Streaming with additional Spark APIs
  • Discover complex Spark Streaming methods

Graph Algorithms: Practical Examples in Apache Spark and Neo4j - Amy E. Hodler, 2019

This hands-on book will teach developers and data scientists how graph analytics can be used to design dynamic network models or forecast real-world behavior. You will work through practical examples demonstrating using Neo4j and Apache Spark's graph algorithms. The learners get to:

  • Understand common graph algorithms and their applications
  • Use example code and tips
  • Discover which algorithms should be applied to certain kinds of queries
  • Use Neo4j and Spark to create an ML process for link prediction

Advanced Analytics with Spark: Patterns for Learning from Data at Scale - Josh Wills, 2017

This edition has been updated for Spark 2.1 and has an overview of Spark programming approaches and best practices. The writers combine statistical techniques, real-world data sets, and Spark to effectively show you how to address analytics challenges. If you have a basic knowledge of machine learning and statistics and programming skills in Java, Python, or Scala, you'll find the book's concepts useful for developing your data applications.

The book will help readers to:

  • Study general data science methodologies
  • Analyze extensive public data sets and look at completed implementations
  • Find machine learning solutions that work with every challenge

Apache Spark in 24 Hours, Sams Teach Yourself - Jeffrey Aven, 2016

The book is designed primarily for anyone seeking knowledge of Apache Spark to construct big data systems efficiently. You will learn how to design innovative approaches that include machine learning, cloud computing, real-time stream processing, and more. The book's in-detail approach demonstrates how to set up, program, improve, manage, integrate, and extend Spark. The readers will learn to:

  • Install and use Spark on-site or in the cloud
  • Engage Spark through the shell
  • Enhance the performance of your Spark solution
  • Explore cutting-edge communications solutions, such as Kafka

Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling - Javier Luraschi, 2019

Data scientists and professionals dealing with massive amounts of data-driven projects can explore leveraging Spark from R to solve big data and significant computation problems by reading this useful book. 

This textbook covers essential data science subjects, cluster computing, and challenges that are relevant to even the most proficient learners. This book is designed for intermediate to expert readers. This book will help the learners to

  • Use R to study, alter, visualize, and evaluate data in Apache Spark
  • Make use of collaborative computing approaches, conduct analysis and modeling across numerous machines
  • Use Spark to easily access a huge volume of data from numerous sources and formats

Spark in Action - Marko Bonaci, 2016

The book provides the knowledge and abilities required to manage batch and streaming data with Spark and has been completely updated for Spark 2.0. In addition to Scala examples, it offers online Java and Python illustrations and real-world case studies on Spark DevOps using Docker. 

The book has been created for professional programmers who have some knowledge of machine learning or big data. Learners can use the book to:

  • Discover how to use Spark to manage batch and streaming data
  • Know the core APIs and Spark CLI
  • Use Spark to implement machine learning algorithms
  • Use Spark to work with graphs and structured data

Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large Scale Data Analysis - Mohammed Guller, 2015

This book provides an overview of Spark and associated big-data technologies. It covers the Spark core and the Spark SQL, Spark Streaming, GraphX, and MLlib add-on libraries.

The textbook is primarily designed for time-pressed professionals who prefer to learn new skills from a single source rather than spending endless hours searching the web for fragments from multiple sources. The user will be able to:

  • Discover the fundamentals of Scala functional programming
  • Use Spark Streaming and Spark Shell to get dynamic visualization

Beginning Apache Spark 3: With Data Frame, Spark SQL, Structured Streaming, and Spark Machine Learning Library - Hien Luu, 2021

This book will teach you about the powerful and efficient distributed data processing engine built into Apache Spark. You will also learn about efficient methods and useful tools for developing machine learning applications. It provides a description of the structured streaming processing engine with tips and techniques for resolving performance problems. This book provides real-world examples and code snippets to help you understand topics and features.

The book is appropriate for readers of intermediate to advanced levels. The book is also used by software developers, data scientists, and data engineers interested in machine learning and big data solutions. Using the book, readers can:

  • Use an extensible data processing engine
  • Supervise the machine learning development process
  • Create big data pipelines

Mastering Apache Spark - Mike Frampton, 2015

This book is for professionals and individuals interested in processing and storing data with Apache Spark. The fundamental Spark components are covered initially, followed by the introduction of some more innovative elements. There are numerous detailed code walkthroughs included that help with comprehension.

Spark's primary components—Machine Learning, Streaming, SQL, and Graph Processing—are covered in detail throughout the book, along with useful code samples. The book is a good fit for intermediate and advanced readers. The readers will get to:

  • Discover how to add experimental components to Spark
  • Understand how Spark integrates with different big-data solutions
  • Explore Spark's prospects in the cloud

Spark Cookbook - Rishi Yadav, 2015

The book includes real-time streaming software samples and Spark SQL code queries. The book offers a variety of machine learning techniques to help readers become acquainted with recommendation engine algorithms. It also has a ton of codes and graphics to help readers whenever they need it. The book readers get:

  • Ways to assess complicated and huge data sets
  • Learn to install and set up Apache Spark using different cluster management
  • Configurations to run Spark SQL interactive queries

Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning Library - Hien Luu, 2018

This book describes using Spark to create cloud-based, adaptable machine learning and analytics systems. The book will demonstrate using Spark SQL for structured data, develop real-time applications with Spark Structured Streaming, and identify resilient distributed datasets (RDDs). In addition, you will learn many other topics, such as the foundations of Spark ML for machine learning. The readers will get to:

  • Understand Spark's integrated data processing platform
  • How to use Databricks or Spark Shell to run Spark
  • Use the Spark Machine Learning package to build creative applications

Mastering Apache Spark 2.x - Romeo Kienzler, 2017

This book will show you how to create machine/deep learning applications and data flows on top of Spark, as well as how to extend its capability. An overview of the Apache Spark ecosystem and the new features and capabilities of Apache Spark 2.x are provided in the book. You will work with the various Apache Spark components, including interactive querying with Spark SQL and efficient use of Data Frames and Data Sets. The readers can learn:

  • Conducting machine learning and deep learning on Spark using MLlib and additional tools like H20
  • Manage memory and graph processing effectively
  • Cloud-based use of Apache Spark

Data Analytics with Spark Using Python - Jeffrey Aven, 2018

The author of this book walks you through all you require to understand how to use Spark, including its extensions, side projects, and larger ecosystems. The book includes a comprehensive set of programming exercises using the well-liked and user-friendly PySpark development environment and a language-neutral overview of fundamental Spark ideas.

Because of its focus on Python, this course is easily accessible to a wide range of data professionals, analysts, and developers, including those with no Hadoop or Spark background. Using the book, learners can:

  • Understand how Spark fits with Big Data ecosystems
  • Learn how to program using the Spark Core RDD API
  • Use SparkR with Spark MLlib to perform predictive modeling

Preparation Tips for Apache Spark

Here are some of the best-known preparation tips to begin with Apache Spark.

  • Learn the fundamentals of Spark architecture and data processing: This includes Spark RDDs, modifications and actions, and Spark SQL.
  • Set up and install PySpark: Installing Spark and configuring PySpark on your local computer or cluster is necessary before you can start using PySpark.
  • Use PySpark examples to practice: As you develop your PySpark skills, start with simpler examples and work your way up to more complicated ones.
  • Using Jupyter Notebooks to create with PySpark: It offers a top-notch setting for PySpark development.
  • Become a member of PySpark communities: This is a great way to learn from other PySpark users and get assistance.
  • Optimize PySpark performance: Follow practices, such as dividing data, caching RDDs, leveraging broadcast elements, and implementing efficient data structures.

More Ways to Learn Spark

With the above list of Spark books, learners can better understand Apache Spark and various Spark applications. But with technological advances, you always have alternatives to fit in your busy days, such as learning Spark and enhancing your skill set. Here are some ways to learn Spark besides using the top Spark books.

  • YouTube: In this age of video making, YouTube has proven to be an incredible platform for learning new concepts in detail with experienced and skilled data experts. These Apache Spark experts guide you step-by-step on how to excel in using Apache Spark for various and multi-dimensional applications. This is one of the best and most inexpensive ways to learn Apache Spark besides Spark books.
  • Tutorials: Innumerable tutorials are available on YouTube, different blog sites, and GitHub repositories. These tutorials provide a detailed understanding of Apache Spark with code snippets, projects, expert guidance, and more components. With GitHub repositories, users can easily access various codes and documentation to crack Apache Spark-related issues.
  • Online Courses: While tutorials and YouTube videos might often make you seem lost in the vast ocean of knowledge, a structured learning course solves the problem. Learners can now easily learn Apache Spark from the start or add to their understanding with the help of the available expert-curated courses. The most popular courses include Simplilearn’s Big Data Hadoop Certification and Training Course
Simplilearn's Professional Certificate Program in Data Engineering, aligned with AWS and Azure certifications, will help all master crucial Data Engineering skills. Explore now to know more about the program.


As you read this article, organizations worldwide generated significant data. With such extensive amounts of data, Apache Spark has been of great use to data scientists and professionals worldwide. There are numerous ways to understand how to use Apache Spark. The best way to start your journey from step one or to advance your understanding is to get started with the top Spark books created by experienced Apache Spark data professionals. If you plan to enhance your career in big data, consider pursuing the Post Graduate Program In Data Engineering or Big Data Hadoop Certification and Training Course.


1. What is the best way to learn Apache Spark?

The best way to learn Apache Spark is through different resources and hands-on approaches to get the best out of the learning.

2. Is Apache Spark tough to learn?

The difficulty level in learning Spark will vary according to your experience level of proficiency. Learning the fundamentals can be challenging yet doable, even for people who have previously done programming and data processing.

3. Should I learn Kafka or Spark?

Kafka is preferable for dependable, low-latency, high-throughput communications across various cloud applications or services. Meanwhile, Spark enables organizations to conduct vast data analysis and machine learning operations.

4. Is Spark easier than Hadoop?

Hadoop is slightly easier than Spark. However, Hadoop offers machine learning features through integration with third-party libraries. At the same time, Spark comes with pre-built machine learning libraries.

5. Which language is better for Spark?

Scala's robust type system, simple syntax, and functional programming characteristics make it the ideal language for Apache Spark. It makes distributed computing scalable and effective. Python's large library and ease of use also make it a popular choice for Spark.