Our editors have compiled this directory of the best Apache Spark books based on Amazon user reviews, rating, and ability to add business value.
There are many free resources available online (such as Solutions Review’s Buyer’s Guide to Business Intelligence and Data Analytics Software, Visual Comparison Matrix and best practices section) and they’re great, but sometimes it’s better to do things the old-fashioned way. There are few resources that can match the comprehensive depth and detail of one of the best Apache Spark data books.
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The editors at Solutions Review have done much of the legwork for you, selecting this directory of the best Apache Spark books on Amazon. titles have been selected based on the total number and quality of reader user reviews and the ability to add business value. Each of the books listed in this compilation has met a minimum criteria of 5 reviews and a rating of 4 stars or higher.
Below you’ll find a library of titles from renowned industry analysts, seasoned practitioners, and subject matter experts ranging from the depths of big data processing to machine learning algorithms. this compilation includes publications for professionals of all levels.
the best books on apache spark
spark: the definitive guide: big data processing made simple
“Learn how to use, implement, and maintain Apache Spark with this comprehensive guide, written by the creators of the open source cluster computing framework. With an emphasis on the enhancements and new features of Spark 2.0, authors Bill Chambers and Matei Zaharia divide Spark topics into distinct sections, each with unique goals. You will explore the basic operations and common functions of Spark Structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications.”
learning spark: blazing-fast big data analytics
“Updated to include spark 3.0, this second edition shows engineers and data scientists why structure and unification in spark is important. specifically, this book explains how to perform simple and complex data analysis and employ machine learning algorithms. Through step-by-step tutorials, code snippets, and notebooks, you’ll be able to learn Python, SQL, Scala, or Java high-level structured APIs and understand Spark operations and the SQL engine, as well as inspect, tune, and debug Spark operations. with spark and spark ui configurations.”
spark in action: covers apache spark 3 with examples in java, python and scala
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“The spark distributed data processing platform provides an easy-to-deploy tool for ingesting, streaming, and processing data from any source. In Spark in Action, you’ll learn how to take advantage of Spark’s core capabilities and its incredible processing speed, with applications including real-time computing, lazy evaluation, and machine learning. Spark skills are a hot commodity in businesses around the world, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without having to learn Scala or Hadoop first.”
high performance spark: best practices for scaling and optimizing apache spark
“apache spark is amazing when everything fits together. But if you haven’t seen the performance improvements you expected, or don’t yet feel confident enough to use Spark in production, this handy 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. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book outlines techniques.”
apache spark in 24 hours, sams learn by yourself
“This book’s simple, step-by-step approach shows you how to implement, program, optimize, manage, integrate, and extend spark now and for years to come. You’ll discover how to build powerful solutions that span cloud computing, real-time stream processing, machine learning, and more. Each lesson builds on what you’ve already learned, giving you a solid foundation for real-world success. Whether you’re a data analyst, data engineer, data scientist, or data manager, the learning spark will help you advance your career.”
frank kane’s big data domination with apache spark and python
“frank kane’s big data taming with apache spark and python is your companion for learning apache spark hands-on. frank will start by teaching you how to set up spark on a single system or across a cluster, and will soon move on to analyzing large data sets with spark rdd and quickly developing and running effective spark jobs with python. frank has packed this book with over 15 fun, interactive examples relevant to the real world, and will help you understand the spark ecosystem.”
graph algorithms: practical examples in apache spark and neo4j
“Learn how graph algorithms can help you harness the relationships within your data to develop intelligent solutions and improve your machine learning models. With this practical guide, developers and data scientists will discover how graph analysis delivers value, whether it’s used to model dynamic networks or forecast real-world behavior. neo4j’s mark needham and amy hodler explain how graph algorithms describe complex structures and reveal hard-to-find patterns.”
Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale
“Practical Data Science with Hadoop and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and Big Data, three leading experts bring together everything you need: high-level concepts, deep dive techniques, real-world use cases, practical applications, and how-to tutorials. The authors introduce the basics of data science and the modern Hadoop ecosystem, and explain how Hadoop and Spark have evolved into a powerful platform for solving data science problems at scale.”
advanced analytics with spark: patterns to learn from data at scale
“In the second edition of this how-to book, four cloudera data scientists present a set of self-contained patterns for performing large-scale data analytics with spark. The authors bring together spark, statistical methods, and real-world data sets to teach you how to approach analytical problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You’ll start with an introduction to spark and its ecosystem, and then dive into patterns that apply common techniques.”
Mastering Machine Learning in AWS: Advanced Machine Learning in Python with Sagemaker, Apache Spark, and Tensorflow
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“As you progress through the chapters, you will learn how these algorithms can be trained, tuned, and implemented on aws using apache spark on elastic map reduce (emr), sagemaker, and tensorflow. While focusing on algorithms such as xgboost, linear models, factoring machines, and deep networks, the book will also provide you with an overview of aws as well as detailed practical applications to help you solve real-world problems. each handy app includes a set of companion notebooks with all the code needed to run on aws.”
mastering spark with r: the complete guide to large-scale modeling and analysis
“if you’re like most r users, you have a deep understanding and love of statistics. But as your organization continues to collect massive amounts of data, it makes a lot of sense to add tools like Apache Spark. With this practical book, data scientists and professionals working with big data applications will learn how to use Spark from R to tackle big data and computing problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems.”
Apache Spark Stream Processing: Mastering Structured Streaming and Spark Streaming
“Before you can build analytics tools for quick insights, you first need to know how to process data in real time. With this hands-on guide, developers familiar with Apache Spark will learn how to use this in-memory framework for streaming data. you will discover how spark allows you to write streaming jobs in much the same way that you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark.”
data analysis with spark using python
“In this guide, big data expert Jeffrey Aven covers everything you need to know to take advantage of Spark, along with its extensions, subprojects, and broader ecosystem. aven combines a language-agnostic introduction to spark basics with extensive programming examples using the popular and intuitive pyspark development environment. This guide’s focus on Python makes it widely accessible to a large number of data professionals, analysts, and developers, even those with little Hadoop or Spark experience.”
scala programming for big data analytics: get started with big data analytics using apache spark
“Get the key concepts of the scala programming language and techniques in the context of big data analytics and apache spark. The book begins with an introduction to Scala and establishes a firm contextual understanding of why you should learn this language, how it compares to Java, and how Scala relates to Apache Spark for big data analytics. Next, you’ll set up the scala environment ready to examine your first scala programs. this is followed by sections on the fundamentals of scala as mutable and immutable variables.”
practical big data analytics: practical techniques for implementing business analytics and machine learning using hadoop, spark, nosql, and r
“With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building enterprise big data and data science platforms. You’ll get hands-on exposure to Hadoop and Spark, build machine learning dashboards with R and Shiny R, build web-based applications with NoSQL databases like MongoDB, and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what big data analytics means.”
hadoop expert management: managing, tuning, and protecting spark, thread, and hdfs
“In hadoop management expert, lead hadoop administrator sam r. alapati brings together authoritative knowledge to create, configure, secure, manage, and optimize production hadoop clusters in any environment. Drawing on his experience with large-scale Hadoop management, Alapati integrates action-oriented advice with carefully researched explanations of problems and solutions. it covers an unrivaled variety of topics and offers an unparalleled collection of realistic examples.”
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