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

Realization of Lambda Architecture

In this section, we will extend our Chicago crime use case to design and code the different layers of Lambda Architecture in Spark.

Let's extend our Chicago crime dataset and assume that the Chicago crime data is delivered in near real-time. Next, our custom consumers will consume the data and will need to find out the number of crimes for each crime category. Though, in most cases, users will require the grouping of data only for the chunk of data received in near real-time, but, in a few use cases, aggregations need to be done on historical data.

Seems like a Lambda use case, doesn't it?

Let's first analyze the complete architecture with all of its components, and then we will describe, code, and execute each and every component of Lambda Architecture.

high-level architecture

In this section, we will discuss the high-level architecture of our Chicago crime use case that is developed using the principles of Lambda Architecture.

We will leverage Spark Streaming...