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 10. Introducing Lambda Architecture

Enterprises have come a long way where there's a demand for a unified system that meets both batch and real-time data processing needs. To extend it further, the need is for a distributed, scalable, highly-available, and fault-tolerant Big Data enterprise system, which is capable of presenting unified views/insights from the batch as well as real-time data systems. Though architects/developers have been working on discrete systems where batch and real-time use cases were developed and deployed separately, presenting this as a single view to the users was a real challenge. Perceiving the objective of a unified view has its own challenges. In some places where it was realized using leveraged traditional architectural patterns, it made the whole system too complex and, in some cases, made the whole system almost unmanageable.

In order to meet the needs and challenges of enterprises for presenting a unified view, which is combination of batch and real...