Scala and Spark for Big Data Analytics

Harness the power of Scala to program Spark and analyze tons of data in the blink of an eye Scala has been witnessing wide-scale adoption over the past few years, particularly in the field of data science and analytics. Spark, which is built on Scala, has also gained recognition, and is now being used widely in production. This book is designed to help you leverage the power of Scala and Spark to make sense of big data. Scala and Spark for Big Data Analytics begins by introducing you to Scala and helping you understand the object-oriented and functional programming concepts required for Spark application development. You'll then move onto Spark and cover basic abstractions using Resilient Distributed Dataset (RDD) and DataFrame. This will help you develop scalable, fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. In the sections to follow, you'll explore advanced topics, such as monitoring, configuration, debugging, testing, and deployment, which will further help you to manage your data effectively. After this, you'll learn to use SparkR and PySpark APIs to develop impactful applications, and deploy Zeppelin to help you create interactive data analytics. Towards the concluding chapters, you'll be able to use Alluxio to facilitate in-memory data processing. By the end of this book, you'll have a clear understanding of Spark and be able to perform full-stack data analytics regardless of the amount of data. If you want to learn how to perform data analysis by harnessing the power of Spark, this is the book for you. Prior knowledge of Spark or Scala is not required. Programming experience (particularly with other Java virtual machine(JVM) languages) will be useful to help you grasp the concepts easily.