Ebook: Mastering Apache Spark 2.x Scale your machine learning and deep learning systems with SparkML, DeepLearning4j and H2O
Author: Romeo Kienzler
- Genre: Computers // Cybernetics: Artificial Intelligence
- Tags: Cloud Computing, Machine Learning, Neural Networks, Deep Learning, Graphs, Classification, Clustering, Apache Spark, Spark ML, Feature Engineering, Stream Processing, Pipelines, Apache Hadoop, Naive Bayes, HDFS, Kubernetes, Spark GraphX, Spark SQL, Spark MLlib, Apache Mesos, Apache YARN, Catalyst Optimizer, Project Tungsten, Kaggle, Apache SystemML, DeepLearning4j, H2O, Spark GraphFrames
- Year: 2017
- Publisher: Packt Publishing
- Edition: 2
- Language: English
- pdf
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 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
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