Treffer: Mastering Apache Spark 2.x Ed. 2

Advanced analytics on your Big Data with latest Apache Spark 2.xAbout This BookAn advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities.Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark.Master the art of real-time processing with the help of Apache Spark 2.xWho 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 LearnExamine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4JStudy highly optimised unified batch and real-time data processing using SparkSQL and Structured StreamingEvaluate large-scale Graph Processing and Analysis using GraphX and GraphFramesApply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM CloudUnderstand internal details of cost based optimizers used in Catalyst, SystemML and GraphFramesLearn how specific parameter settings affect overall performance of an Apache Spark clusterLeverage Scala, R and python for your data science projectsIn DetailApache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform.The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x.You will understand how memory management and binary processing, cache-aware