- 332 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Mastering Apache Spark 2.x - Second Edition
About This Book
Advanced analytics on your Big Data with latest Apache Spark 2.xAbout 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.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 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 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 computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets.You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks.Style and approachThis book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.
Frequently asked questions
Information
Deep Learning on Apache Spark with DeepLearning4j and H2O
- Introduction to the installation and usage of the H2O framework
- Introduction to Deeplearning4j with an IoT anomaly detection example
H2O
Overview
- Process
- Model
- The score tool
- Data profiling
- Generalized linear models (GLM)
- Predict
- Summary statistics
- Decision trees
- Confusion matrix
- Aggregate, filter, bin, and derive columns
- Gradient boosting machine (GBM)
- AUC
- Slice, log transform, and anonymize
- K-means
- Hit ratio
- Variable creation
- Anomaly detection
- PCA/PCA score
- Deep learning
- Multimodel scoring
- Training and validation sampling plan
- Naive Bayes
- Grid search
[hadoop@hc2r1m2 h2o]$ pwd ; ls -l
/home/hadoop/h2o
total 15892
-rw-r--r-- 1 hadoop hadoop 16272364 Apr 11 12:37 sparkling-water-0.2.12-92.zip
[hadoop@hc2r1m2 h2o]$ unzip sparkling-water-0.2.12-92.zip
[hadoop@hc2r1m2 h2o]$ ls -d sparkling-water*
sparkling-water-0.2.12-92 sparkling-water-0.2.12-92.zip
[hadoop@hc2r1m2 h2o]$ su -
[root@hc2r1m2 ~]# cd /home/hadoop/h2o
[root@hc2r1m2 h2o]# mv sparkling-water-0.2.12-92 /usr/local
[root@hc2r1m2 h2o]# cd /usr/local
[root@hc2r1m2 local]# chown -R hadoop:hadoop sparkling-water-0.2.12-92
[root@hc2r1m2 local]# ln âs sparkling-water-0.2.12-92 h2o
[root@hc2r1m2 local]# ls âlrt | grep sparkling
total 52
drwxr-xr-x 6 hadoop hadoop 4096 Mar 28 02:27 sparkling-water-0.2.12-92
lrwxrwxrwx 1 root root 25 Apr 11 12:43 h2o -> sparkling-water-0.2.12-92
The build environment
[hadoop@hc2r1m2 h2o_spark_1_2]$ pwd
/home/hadoop/spark/h2o_spark_1_2
[hadoop@hc2r1m2 h2o_spark_1_2]$ more h2o.sbt
name := "H 2 O"
version := "1.0"
scalaVersion := "2.10.4"
libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "2.3.0"
libraryDependencies += "org.apache.spark" % "spark-core" % "1.2.0" from "file:///usr/hdp/2.6.0.3-8/spark/lib/spark-assembly-1.6.3.2.6.0.3-8-hadoop2.7.3.2.6.0.3-8.jar"
libraryDependencies += "org.apache.spark" % "mllib" % "1.2.0" from "file:///usr/hdp/2.6.0.3-8/spark/lib/spark-assembly-1.6.3.2.6.0.3-8-hadoop2.7.3.2.6.0.3-8.jar"
libraryDependencies += "org.apache.spark" % "sql" % "1.2.0" from "file:///usr/hdp/2.6.0.3-8/spark/lib/spark-assembly-1.6.3.2.6.0.3-8-hadoop2.7.3.2.6.0.3-8.jar"
libraryDependencies += "org.apache.spark" % "h2o" % "0.2.12-95" from "file:///usr/local/h2o/assembly/build/libs/sparkling-water-assembly-0.2.12-95-all.jar"
libraryDependencies += "hex.deeplearning" % "DeepLearningModel" % "0.2.12-95" from "file:///usr/local/h2o/assembly/build/libs/sparkling-water-assembly-...
Table of contents
- Title Page
- Copyright
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Preface
- A First Taste and Whatâs New in Apache Spark V2
- Apache Spark SQL
- The Catalyst Optimizer
- Project Tungsten
- Apache Spark Streaming
- Structured Streaming
- Apache Spark MLlib
- Apache SparkML
- Apache SystemML
- Deep Learning on Apache Spark with DeepLearning4j and H2O
- Apache Spark GraphX
- Apache Spark GraphFrames
- Apache Spark with Jupyter Notebooks on IBM DataScience Experience
- Apache Spark on Kubernetes