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

Storm architecture and its components

We have discussed enough about the history and theory of abstractions of Storm; it's now time to dive in and see the framework in execution and get hands on to the real code to actually see Storm in action. We are just one step away from the action part. Before we get there, let's understand what are the various components that get to play in Storm and what is their contribution in the building and orchestration of this framework.

Storm execution can be done in two flavors:

  • Local mode: This is a single node and a nondistributed setup that is generally used for demo and testing. Here, the entire topology is executed in a single worker and thus a single JVM.

  • Distributed mode: This is a multinode setup that is fully or partially distributed and this is the recommended mode for real-time application development and deployment.

The instructions can be referred to from the Apache Storm site at