Title: Mastering Apache Spark 2.x - Second Edition: Scale your machine learning and deep learning systems with SparkML, DeepLearning4j and H2O
Publisher: Packt Publishing
Publication Date: 2017
Language: English
ISBN 10: 1786462745
ISBN 13: 9781786462749
Binding: Soft cover
Edition: 2nd Edition
Condition: New

About this title

Synopsis

Advanced analytics on your Big Data with latest Apache Spark 2.x

About This Book

An 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.x

Who This Book Is For

If 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 Learn

Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J
Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming
Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames
Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud
Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames
Learn how specific parameter settings affect overall performance of an Apache Spark cluster
Leverage Scala, R and python for your data science projects

In Detail

Apache 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

About the Author

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics. His current research focus is on scalable machine learning on Apache Spark. He is a contributor to various open source projects and works as an associate professor for artificial intelligence at Swiss University of Applied Sciences, Berne. He is a member of the IBM Technical Expert Council and the IBM Academy of Technology, IBM's leading brains trust.

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