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

The technology matrix for Lambda Architecture

In this section, we will discuss various technology options available for developing the various layers of Lambda Architecture.

Lambda Architecture talks about four different layers, and each layer has its own function and purpose. Let's look at the variety of technologies available that can be leveraged for developing these layers of Lambda Architecture:

The data consumption layer is the first layer in the overall architecture. Going by the name, it seems to be the simplest layer, but, in reality, it needs to deal with a lot of complexities. Here are a few challenges that we need to keep in mind before developing or choosing any technology for the data consumption layer:

  • Highly available: It should be highly available and it should be ensured that it works either in master, slave, or peer architecture. There should be no single point of failure that can stop the consumption of messages.

  • Fault tolerant: It is extremely important that it is fault...