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

Introducing R and SparkR

Let's understand the features and limitations of R and how SparkR helps in overcoming those limitations.

What is R?

R is an open source software package for statistical analysis, machine learning, and visualization of data. R project ( is a simple programming language, such as S and S-plus. R can be used on multiple platforms such as Windows, Linux, Mac OS, and other Unix flavors. R was originally developed at the University of Auckland by Ross Ihaka and Robert Gentleman, and now it is maintained by the R development team. It is an implementation of S language, which was developed by John Chambers. R is an interpreted programming language, and is one of the most popular open source statistical analysis packages.

The R features are as follows:

  • Open source with over 7,000 packages

  • Stable statistics, graphics, and general packages

  • Manipulates R objects directly in C, C++, and Java

  • Command line and IDEs support

Limitations of R, are as follows:

  • Single...