Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Scientific Computing and Big Data Analysis with Python and Hadoop

Methods of integrating R and Hadoop

Data analysts or data scientists working with Hadoop might have R packages or R scripts that they use for data processing. To use these R scripts or R packages with Hadoop, they need to rewrite these R scripts in the Java programming language or any other language that implements Hadoop MapReduce. This is a burdensome process and could lead to unwanted errors. To integrate Hadoop with the R programming language, we need to use a software that is already written for R, with the data being stored in the distributed of storage Hadoop. There are many solutions for using the R language to perform large computations, but all these solutions require that the data be loaded into the memory before it is distributed to the computing nodes. This is not an ideal solution for large datasets. Here are some commonly used methods to integrate Hadoop with R to make the best use of the analytical capabilities of R for large datasets.

RHADOOP – install R on workstations and...