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Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics

By : Karim, Sridhar Alla
2.8 (12)
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Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics

2.8 (12)
By: Karim, Sridhar Alla

Overview of this book

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. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
Table of Contents (19 chapters)
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Apache Spark installation

Apache Spark is a cross-platform framework, which can be deployed on Linux, Windows, and a Mac Machine as long as we have Java installed on the machine. In this section, we will look at how to install Apache Spark.

Apache Spark can be downloaded from http://spark.apache.org/downloads.html

First, let's look at the pre-requisites that must be available on the machine:

  • Java 8+ (mandatory as all Spark software runs as JVM processes)
  • Python 3.4+ (optional and used only when you want to use PySpark)
  • R 3.1+ (optional and used only when you want to use SparkR)
  • Scala 2.11+ (optional and used only to write programs for Spark)

Spark can be deployed in three primary deployment modes, which we will look at:

  • Spark standalone
  • Spark on YARN
  • Spark on Mesos

Spark standalone...

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