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

What is Lambda Architecture

In this section, we will talk about the various features and components of Lambda Architecture. Let's move forward and first look at the need for Lambda Architecture, then, we will dive deep into the various other aspects of it.

The need for Lambda Architecture

The driving force behind Lambda was the latency introduced by the MapReduce paradigm. Hadoop or MapReduce solved the purpose of a distributed and scalable batch processing system, but, at the same time, it was also true that batch views were created on stale and outdated data (by at least 3-4 hours). Though it was acceptable in a few cases where data arrived once or twice in a day, but it was not acceptable to use cases where real-time updates could make a significant difference to the overall computations.

The next evident question is as to why we can't compute and recompute everything on the fly.

We can only do this if our systems have unlimited CPU, memory, and network speed, which obviously is not the case...