Key FeaturesExperience Scala's sophisticated type system, combining functional programming and object-oriented conceptsWork on an array of applications, ranging from simple batch jobs to stream processing and machine learningPerform large-scale data analysis by exploring both common as well as complex use-casesBook DescriptionScala 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.What you will learnGet an in-depth understanding of Scala collection APIsWork with RDD and DataFrame to learn Spark's core abstractionsAnalyse structured and unstructured data using SparkSQL and GraphXBuild scalable and fault-tolerant streaming applications using Spark structured streamingDiscover machine-learning (ML) best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib and MLDevelop clustering models to cluster a vast amount of dataGet to grips with tuning, debugging, and monitoring Spark applicationsDeploy Spark applications on real clusters in Standalone, Mesos, and Yet Another Resource Negotiator (YARN)Who this book is forIf 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.Table of ContentsIntroduction to ScalaObject-Oriented ScalaFunctional Programming ConceptsCollection APIsTackle Big Data - Spark Comes to the PartyStart Working with Spark - REPL and RDDsSpecial RDD OperationsIntroduce a Little Structure - Spark SQLStream Me Up, Scotty - Spark StreamingEverything is Connected - GraphXLearning Machine Learning - Spark MLlib and Spark MLMy Name is Bayes, Naive BayesTime to Put Some Order - Cluster Your Data with Spark MLlibText Analytics Using Spark MLSpark TuningTime to Go to ClusterLand - Deploying Spark on a ClusterTesting and Debugging SparkPySpark and SparkR