Last Updated: January 30, 2019

Apache Spark is an open-source cluster-computing framework made in 2014. It was originally developed at the University of California, Berkeley’s AMPLab. Spark is now maintained by the Apache Software Foundation. It is a super fast analytics engine used for Big Data and Machine Learning. Spark provides high-level APIs in Scala, Java, Python, and R. Spark has modules which include Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing. Here are the best Apache Spark tutorials, books & courses in 2019.

 

Best Apache Spark courses & tutorials 2019

Apache Spark 2.0 with Scala – Hands On with Big Data!

Apache Spark 2.0 with Scala – Hands On with Big Data! by Frank Kane will teach you to analyze large data sets with Apache Spark and Scala. You will learn to frame big data analysis problems as Apache Spark scripts. This Apache Spark tutorial will also teach you the Scala programming language. Scala and Spark work together very well. Using Scala, you will develop distributed code. You will learn the concepts of Spark’s Resilient Distributed Datastores. This Spark tutorial will teach you how to carry out partitioning, caching, and other techniques to optimize your Spark jobs. You will learn how to build, deploy, and run Spark scripts on Hadoop clusters. This Spark course will teach you to use Spark Streaming. Spark Streaming allows you to process continual streams of data. This Apache Spark 2 tutorial will help you transform structured data using SparkSQL and DataFrames. Using GraphX, you will traverse and analyze graph structure. You will make use of Amazon’s Elastic MapReduce service for larger data sets. The Spark & Scala tutorial is packed with over 20 real world examples. By the end of this Spark tutorial, you will be able to analyze gigabytes of data in cloud in a few minutes. This is the best Apache Spark & Scala tutorial in 2019.

 

Apache Spark with Java – Learn Spark from a Big Data Guru

Apache Spark with Java – Learn Spark from a Big Data Guru by James Lee and Tao W. will teach you everything you need to know about developing Spark applications with Java. You will start of with an overview of Apache Spark architecture. This Apache Spark tutorial will teach you to develop Apache Spark 2.0 applications with Java and Spark SQL. You will make use of Resilient Distributed Datasets(RDDs) to process and analyze large data sets. Advanced Spark techniques like partitioning, caching and persisting RDDs will be used to optimize your Spark jobs. You will gain a good understanding of Spark SQL. By using broadcast variables and accumulators, you will share data across different nodes of Spark clusters. By the end of this Spark course, you will gain in-depth Spark knowledge and the ability to carry out Spark jobs. This Spark tutorial will teach you Spark best practices.This is the best Apache Spark and Java tutorial in 2019.

 

Taming Big Data with Apache Spark and Python – Hands On!

Taming Big Data with Apache Spark and Python – Hands On! by Frank Kane will teach you to analyze large data sets with Apache Spark. This Apache Spark tutorial uses Python to develop and run Spark jobs. You will learn to frame big data analysis problems into Spark problems. The Apache Spark course teaches you how to make use of Spark’s Resilient Distributed Datasets. This allows you to process and analyze data sets across multiple CPUs to get even more processing power. You will make use of the MLLib machine learning library. The MLLib machine learning library allows you to answer common data mining questions. This Spark tutorial will teach you to make use of Spark SQL and Spark Streaming. You will learn to troubleshoot errors that may occur from running large Spark jobs on a cluster. This Spark course will teach you to implement iterative algorithms in Spark. This is the best Apache Spark & Python tutorial in 2019.

Best Apache Spark books 2019

Bestsellers

SaleBestseller No. 1
Advanced Analytics with Spark: Patterns for Learning from Data at Scale
  • OREILLY
  • Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills
  • Publisher: O'Reilly Media
  • Edition no. 2 (07/06/2017)
  • Paperback: 280 pages
SaleBestseller No. 2
High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
  • Holden Karau, Rachel Warren
  • Publisher: O'Reilly Media
  • Edition no. 1 (06/16/2017)
  • Paperback: 358 pages
SaleBestseller No. 3
Spark: The Definitive Guide: Big Data Processing Made Simple
  • Bill Chambers, Matei Zaharia
  • Publisher: O'Reilly Media
  • Edition no. 1 (03/08/2018)
  • Paperback: 606 pages
Bestseller No. 4
Frank Kane's Taming Big Data with Apache Spark and Python
  • Frank Kane
  • Publisher: Packt Publishing
  • Paperback: 296 pages
SaleBestseller No. 5
Learning Spark: Lightning-Fast Big Data Analysis
  • O Reilly Media
  • Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia
  • Publisher: O'Reilly Media
  • Edition no. 1 (02/27/2015)
  • Paperback: 276 pages
Bestseller No. 6
Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing
  • Tyler Akidau, Slava Chernyak, Reuven Lax
  • O'Reilly Media
  • Kindle Edition
  • Edition no. 1 (07/16/2018)
  • English
SaleBestseller No. 8
Practical Apache Spark: Using the Scala API
  • Subhashini Chellappan
  • Publisher: Apress
  • Edition no. 1 (12/13/2018)
  • Paperback: 296 pages
SaleBestseller No. 9
Apache Spark in 24 Hours, Sams Teach Yourself
  • Jeffrey Aven
  • Publisher: Sams Publishing
  • Edition no. 1 (08/27/2016)
  • Paperback: 592 pages
Bestseller No. 10

 

 

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Last update on 2019-05-26 / Affiliate links / Images from Amazon Product Advertising API