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


Transformations on DStreams are similar to those that are applicable to a Spark Core RDD. DStreams consist of RDDs, so a transformation applies to each RDD to generate a transformed RDD for each RDD, creating a transformed DStream. Each transformation creates a specified DStream derived class.

There are many DStream classes that are built for a functionalities; map transformations, window functions, reduce actions, and different InputStream types are implemented using different DStream-derived classes.

The following table showcases the possible  types of transformations:




Applies the transformation function to each element of the DStream and returns a new DStream.


Filters out the records of the DStream to return a new DStream.


Creates more or fewer partitions to redistribute the data to change the parallelism.


Combines the elements in two source DStreams and returns a new DStream.