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

Writing and executing our first Spark program

In this section, we will install/configure and write our first Spark program in Java and Scala.

Hardware requirements

Spark supports a variety of hardware and software platforms. It can be deployed on commodity hardware and also supports deployments on high-end servers. Spark clusters can be provisioned either on cloud or on-premises. Though there is no single configuration or standard, which can guide us through the requirements of Spark, to create and execute Spark examples provided in this book, it would be good to have a laptop/desktop/server with the following configuration:

  • RAM: 8 GB.

  • CPU: Dual core or Quad core.

  • DISK: SATA drives with a capacity of 300 GB to 500 GB with 15 k RPM.

  • Operating system: Spark supports a variety of platforms that include various flavors of Linux (Ubuntu, HP-UX, RHEL, and many more) and Windows. For our examples, we will recommend that you use Ubuntu for the deployment and execution of examples.

Spark core is coded...