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

SparkSQL and DataFrames


Before Apache Spark, Apache Hive was the go-to technology whenever anyone wanted to run an SQL-like query on large amount of data. Apache Hive essentially translated an SQL query into MapReduce, like logic automatically making it very easy to perform many kinds of analytics on big data without actually learning to write complex code in Java and Scala. 

With the advent of Apache Spark, there was a paradigm shift in how we could perform analysis at a big data scale. Spark SQL provides an SQL-like layer on top of Apache Spark's distributed computation abilities that is rather simple to use. In fact, Spark SQL can be used as an online analytical processing database. Spark SQL works by parsing the SQL-like statement into an abstract syntax tree (AST), subsequently converting that plan to a logical plan and then optimizing the logical plan into a physical plan that can be executed, as shown in the following diagram:

The final execution uses the underlying DataFrame API, making...