Book Image

Big Data Analytics

By : Venkat Ankam
Book Image

Big Data Analytics

By: Venkat Ankam

Overview of this book

Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.
Table of Contents (18 chapters)
Big Data Analytics
About the Author
About the Reviewers

Machine learning with SparkR

Spark version 1.5 added support for machine learning over DataFrames created in SparkR. SparkR currently supports the Generalized Linear Model, Accelerated Failure Time (AFT), Survival Regression Model, Naive Bayes Model, and K-Means algorithms in version 2.0.

Let's go through a couple of examples to understand how machine learning is implemented in SparkR.

Using the Naive Bayes model

Based on the Titanic survival dataset, let's analyze what sorts of people are likely to survive. The Titanic dataset is summarized according to economic status (class), sex, age, and survival. spark.naiveBayes() fits a Bernoulli Naive Bayes model against a Spark DataFrame. The steps to do so are as follows:

  1. Create a local DataFrame and convert it to a Spark DataFrame:

    > localDF <-
    > DF <- createDataFrame(localDF[localDF$Freq > 0, -5])
    > head(DF)
      Class    Sex   Age Survived
    1   3rd   Male Child       No
    2   3rd Female Child       No
    3   1st...