In this post, we have prepared a curated top list of reading recommendations for beginners and experienced. This hand-picked list of the best Spark books and tutorials can help fill your brain this May and ensure you’re getting smarter. We have also mentioned the brief introduction of each book based on the relevant Amazon or Reddit descriptions.
- Advanced Analytics with Spark: Patterns for Learning from Data at Scale (2017)
- Spark: The Definitive Guide: Big Data Processing Made Simple (2018)
- High Performance Spark (2017)
- Scala and Spark for Big Data Analytics (2017)
- A collection of Advanced Data Science and Machine Learning (2015)
- Learning Spark: Lightning-Fast Big Data Analysis (2015)
- PySpark Recipes (2017)
- SPARK 2014 User’s Guide (2017)
- Big Data Analytics with Spark (2015)
- Apache Spark in 24 Hours (2016)
- Mastering Azure Analytics (2017)
- Spark GraphX in Action (2016)
Advanced Analytics with Spark: Patterns for Learning from Data at Scale (2017)
In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.
Author(s): Sandy Ryza, Uri Laserson
Spark: The Definitive Guide: Big Data Processing Made Simple (2018)
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of this open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.
Author(s): Bill Chambers, Matei Zaharia
High Performance Spark (2017)
Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources.
Author(s): Holden Karau, Rachel Warren
Scala and Spark for Big Data Analytics (2017)
Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions.
Author(s): Md. Rezaul Karim, Sridhar Alla
A collection of Advanced Data Science and Machine Learning (2015)
A collection of Machine Learning interview questions in Python and Spark
Author(s): Dr Antonio Gulli
Learning Spark: Lightning-Fast Big Data Analysis (2015)
Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala.
Author(s): Holden Karau, Andy Konwinski
PySpark Recipes (2017)
Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented.
Author(s): Raju Kumar Mishra
SPARK 2014 User’s Guide (2017)
SPARK 2014 is a programming language and a set of verification tools designed to meet the needs of high-assurance software development. SPARK 2014 is based on Ada 2012, both subsetting the language to remove features that defy verification, but also extending the system of contracts and aspects to support modular, formal verification.The new aspects support abstraction and refinement and facilitate deep static analysis to be performed including flow analysis and formal verification of an implementation against a specification.
Author(s): AdaCore Team, Altran UK Ltd
Big Data Analytics with Spark (2015)
Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert.Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding.
Author(s): Mohammed Guller
Apache Spark in 24 Hours (2016)
Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date.
Author(s): Jeffrey Aven
Mastering Azure Analytics (2017)
Microsoft Azure has over 20 platform-as-a-service (PaaS) offerings that can act in support of a big data analytics solution. So which one is right for your project? This practical book helps you understand the breadth of Azure services by organizing them into a reference framework you can use when crafting your own big data analytics solution.
Author(s): Zoiner Tejada
Spark GraphX in Action (2016)
Spark GraphX in Action starts out with an overview of Apache Spark and the GraphX graph processing API. This example-based tutorial then teaches you how to configure GraphX and how to use it interactively. Along the way, you’ll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data.GraphX is a powerful graph processing API for the Apache Spark analytics engine that lets you draw insights from large datasets.
Author(s): Michael Malak, Robin East
Best Spark Books You Must Read
We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Library Genesis and download some Spark books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.
Dream. Explore. Discover.: Inspiring Quotes to Spark Your Wanderlust
Author(s): Summersdale Publishers
ID: 2394224, Publisher: Summersdale Publishers, Year: 9 July 2019, Size: 30 Mb, Format: epub
A Terrible Thing to Waste: Environmental Racism and Its Assault on the American Mind
Author(s): Harriet A. Washington
ID: 2392508, Publisher: Little, Brown Spark, Year: 23 July 2019, Size: 25 Mb, Format: epub
Scaling Machine Learning with Spark
Author(s): Adi Polak
ID: 3332465, Publisher: O'Reilly Media, Inc., Year: 2023, Size: 5 Mb, Format: epub
Please note that this booklist is not definite. Some books are absolutely record-breakers according to Washington Post, others are drafted by unknown authors. On top of that, you can always find additional tutorials and courses on Coursera, Udemy or edX, for example. Are there any other relevant resources you could recommend? Drop a comment if you have any feedback on the list.