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

Introduction to data analytics

Data analytics is the process of applying qualitative and quantitative techniques when examining data, with the goal of providing valuable insights. Using various techniques and concepts, data analytics can provide the means to explore the data exploratory data analysis (EDA) as well as draw conclusions about the data confirmatory data analysis (CDA). The EDA and CDA are fundamental concepts of data analytics, and it is important to understand the differences between the two.

EDA involves the methodologies, tools, and techniques used to explore data with the intention of finding patterns in the data and relationships between various elements of the data. CDA involves the methodologies, tools, and techniques used to provide an insight or conclusion for a specific question, based on hypothesis and statistical techniques, or simple observation of the data.

Inside the data analytics process

Once data is deemed ready, it can be analyzed and explored by data scientists...