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

Chapter 6. Batch Analytics with Apache Spark

In this chapter, you will learn about Apache Spark and how to use it for big data analytics based on a batch processing model. Spark SQL is a component on top of Spark Core that can be used to query structured data. It is becoming the de facto tool, replacing Hive as the choice for batch analytics on Hadoop.

Moreover, you will learn how to use Spark for the analysis of structured data (unstructured data such as a document containing arbitrary text, or some other format that has to be transformed into a structured form). We will see how DataFrames/datasets are the cornerstone here, and how SparkSQL's APIs make querying structured data simple yet robust.

We will also introduce datasets and see the difference between datasets, DataFrames, and RDDs. In a nutshell, the following topics will be covered in this chapter:

  • SparkSQL and DataFrames
  • DataFrames and the SQL API
  • DataFrame schema
  • Datasets and encoders
  • Loading and saving data
  • Aggregations
  • Joins