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 1. Introducing the Big Data Technology Landscape and Analytics Platform

The Big Data paradigm has emerged as one of the most powerful in next-generation data storage, management, and analytics. IT powerhouses have actually embraced the change and have accepted that it's here to stay.

What arrived just as Hadoop, a storage and distributed processing platform, has really graduated and evolved. Today, we have whole panorama of various tools and technologies that specialize in various specific verticals of the Big Data space.

In this chapter, you will become acquainted with the technology landscape of Big Data and analytics platforms. We will start by introducing the user to the infrastructure, the processing components, and the advent of Big Data. We will also discuss the needs and use cases for near real-time analysis.

This chapter will cover the following points that will help you to understand the Big Data technology landscape:

  • Infrastructure of Big Data

  • Components of the Big Data ecosystem

  • Analytics architecture

  • Distributed batch processing

  • Distributed databases (NoSQL)

  • Real-time and stream processing