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

Data analytics

R allows us to conduct a wide variety of data analytics. Everything we have done with pandas in Python, we are able to do in R as well.

Take a look at the following code:

df = read.csv(file=file.choose(), header=T, fill=T, sep=",", stringsAsFactors=F)

file.choose() means there will be a new window that will allow you to select the data file to be opened. header=T means it will read the header. fill=T means it will fill in NaN for any undefined or missing data values. Finally, sep="," means that it knows how to distinguish between the different data values in the .csv file. In this case, they are all separated by commas. stringsAsFactors tells it to treat all the string values as strings, not as factors. This allows us to replace values in the data later on.

Now, you should see this:

Figure: Screenshot of output you will obtain

Press Enter. You should see something like this if you are on Windows:

Regardless of the OS, you should see a window that opens up to allow you to choose a...