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

High-level architecture

In this section, we will talk about the high-level architecture of Spark Streaming. We will also discuss the important components of Spark Streaming such as Discretized Streams, microbatching, and more. At the end, we will also write our first Spark streaming job for consuming and processing data in near real-time.

Spark Streaming is one of the powerful extensions provided by Spark for consuming and processing the events produced by various data sources in near real-time. Spark Streaming extended the Spark core architecture and produced a new architecture based on microbatching, where live/streaming data is received and collected from various data sources and further divided into a series of deterministic microbatches. The size of each microbatch is essentially governed by the batch duration provided by the user. In order to understand it better, let's take an example of an application receiving live/streaming data of 20 events per second where the batch duration provided...