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

An overview of Spark

In this section, we will talk about Spark and its emergence as one of the leading frameworks for various kinds of Big Data use cases. We will also talk about the various features of Spark and its applicability in different scenarios.

Another distributed framework for crunching large data? Another version of Hadoop?

This is the first statement that comes to mind when we hear about Spark for the first time, but this is not true and neither is there any essence. We will soon talk more about this statement, but before that, let's first understand batch processing and real-time data processing.

Batch data processing

Batch data processing is a process of defining a series of jobs that are executed one after another or in parallel in order to achieve a common goal. Mostly, these jobs are automated and there is no manual intervention. These jobs collect the input data and process the data in batches where the size of each batch can vary. It can range from a few GBs to TBs/PBs. These...