Scala and Spark for Big Data Analytics

Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye!

About This Book
Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts
Work on a wide array of applications, from simple batch jobs to stream processing and machine learning
Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark
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
In Detail
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.

Style and approach
Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.