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About This BookExplore the integration of Apache Spark with third party applications such as H20, Databricks and TitanEvaluate how Cassandra and Hbase can be used for storageAn advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalitiesWho This Book Is ForIf you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected.What You Will LearnExtend the tools available for processing and storageExamine clustering and classification using MLlibDiscover Spark stream processing via Flume and HDFSCreate a schema in Spark SQL and learn how a Spark schema can be populated with dataStudy Spark-based graph processing using Spark GraphXCombine Spark with H20 and deep learning and learn why it is usefulEvaluate how graph storage works with Apache Spark, Titan, HBase, and CassandraUse Apache Spark in the cloud with Databricks and AWSIn DetailApache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionality, such as graph processing, machine learning, stream processing, and SQL.This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark's functionality. The book commences with an overview of the Spark ecosystem. You will learn how to use MLlib to create a fully-working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph-based storage, Databricks for cloud-based Spark. Intermediate Scala-based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment. |
