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

Schema – structure of data

A schema is the description of the structure of your data and can be either implicit or explicit. There are two main ways to convert existing RDDs into datasets as the DataFrames are internally based on the RDD; they are as follows:

  • Using reflection to infer the schema of the RDD
  • Through a programmatic interface with the help of which you can take an existing RDD and render a schema to convert the RDD into a dataset with schema

Implicit schema

Let's look at an example of loading a comma-separated values (CSV) file into a DataFrame. Whenever a text file contains a header, the read API can infer the schema by reading the header line. We also have the option to specify the separator to be used to split the text file lines.

We read the csv inferring the schema from the header line and use the comma (,) as the separator. We also show the use of the schema command and the printSchema command to verify the schema of the input file:

scala> val statesDF =