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

Chapter 9. Analysis of Streaming Data Using Spark Streaming

Enterprises in today's modern era consume data from a variety of data sources. The delivery of data from these sources is not only in different formats (CSV, text, Excel, and so on), but at the same time they may provide different mechanisms for data consumption. For example, some data sources may provide a particular location on the shared filesystem or some may provide data streams ( or queuing-based systems.

Though there are tools and technologies to handle the complexities of data consumption, the real challenge is always to have a single solution/platform that can meet and solve all problems. Enterprises have been focusing on developing/deploying a single platform that is flexible and extendable to handle all complexities of data consumption/processing and produce it in a unified format.

Spark with its extensions is emerging as a one-stop solution to meet all the requirements of enterprises...