- Scala And Spark For Big Data Analytics

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Scala And Spark For Big Data Analytics is published by on July 25, 2017. This book has 898 pages in English, ISBN-10 1785280848, ISBN-13 978-1785280849. AZW is available for download below.

Scala And Spark For Big Data Analytics.

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.

Who This Book Is For

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.

What You Will Learn

  • Understand object-oriented & functional programming concepts of Scala
  • In-depth understanding of Scala collection APIs
  • Work with RDD and DataFrame to learn Spark’s core abstractions
  • Analysing structured and unstructured data using SparkSQL and GraphX
  • Scalable and fault-tolerant streaming application development using Spark structured streaming
  • Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML
  • Build clustering models to cluster a vast amount of data
  • Understand tuning, debugging, and monitoring Spark applications
  • Deploy Spark applications on real clusters in Standalone, Mesos, and YARN



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