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
Contributors
Preface
4
Scientific Computing and Big Data Analysis with Python and Hadoop
Index

Summary


In this chapter, we have discussed the origin of DataFrames and how Spark SQL provides the SQL interface on top of DataFrame. The power of DataFrames is such that the execution times have decreased over the original RDD-based computations. Having such a powerful layer with a simple SQL-like interface makes it all the more powerful. We also looked at various APIs to create and manipulate DataFrames and dug deeper into the sophisticated features of aggregations, including groupBy, Window, rollup, and cubes. Finally, we also looked at the concept of joining datasets and the various types of joins possible such as inner, outer, cross, and so on.

We will explore the exciting world of real-time data processing and analytics in Chapter 7Real-Time Analytics with Apache Spark.