Book Image

Real-Time Big Data Analytics

By : Sumit Gupta, Shilpi Saxena
Book Image

Real-Time Big Data Analytics

By: Sumit Gupta, Shilpi Saxena

Overview of this book

Enterprise has been striving hard to deal with the challenges of data arriving in real time or near real time. Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases. From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm. Moving on, we’ll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program. You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark. At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data.
Table of Contents (17 chapters)
Real-Time Big Data Analytics
About the Authors
About the Reviewer

Coding our first Spark SQL job

In this section, we will discuss the basics of writing/coding Spark SQL jobs in Scala and Java. Spark SQL exposes the rich DataFrame API ( for loading and analyzing datasets in various forms. It not only provides operations for loading/analyzing data from structured formats such as Hive, Parquet, and RDBMS, but also provides flexibility to load data from semistructured formats such as JSON and CSV. In addition to the various explicit operations exposed by the DataFrame API, it also facilitates the execution of SQL queries against the data loaded in the Spark.

Let's move ahead and code our first Spark SQL job in Scala and then we will also look at the corresponding implementation in Java.

Coding a Spark SQL job in Scala

In this section, we will code and execute our first Spark SQL Job using Scala APIs.

It is our first Spark SQL job, so we will make it simple and use some sample...