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


Hive provides a SQL layer abstraction over the MapReduce framework with several optimizations. This is needed because of the complexity of writing code using the MapReduce framework. For example, a simple count of the records in a specific file takes at least a few dozen lines of code, which is not productive to anyone. Hive abstracts the MapReduce code by encapsulating the logic from the SQL statement into a MapReduce framework code, which is automatically generated and executed on the backend. This saves incredible amounts of time for anyone who needs to spend more time on doing something useful with the data, rather than going through the boiler plate coding for every single task that needs to be executed and every single computation that's desired as part of your job:

Hive is not designed for online transaction processing and does not offer real-time queries and row-level updates.

In this section, we will look at Hive and how to use it to perform analytics,