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

MapReduce patterns

A MapReduce pattern is a template for solving a common and general data manipulation problem with MapReduce. A pattern is not specific to a domain, such as text processing or graph analysis, but it is a general approach to solving a problem. Using design patterns is all about using tried and true design principles to build better software. 

Design patterns have been making developers, lives easier for years. They are tools for solving problems in a reusable and general way, so that the developer can spend less time figuring out how they're going to overcome a hurdle and move on to the next one.

Aggregation patterns

This chapter focuses on design patterns that produce a top-level, summarized view of your data, so you can glean insights not available from looking at a localized set of records alone. Aggregation, or summarization, analytics are all about grouping similar data together and then performing an operation, such as calculating a statistic, building an index, or simply...