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

Querying streaming data in real time

In this section, we will extend our Chicago crime example and will perform some real-time analysis using Spark SQL on the streaming crime data.

All Spark extensions extend a core architecture component of Spark: RDD. Now whether it is DStreams in Spark Streaming or DataFrame in Spark SQL, they are interoperable with each other. We can easily convert DStreams into DataFrames and vice versa. Let's move ahead and understand the integration architecture of Spark Streaming and Spark SQL. We will also materialize the same and develop an application for querying streaming data in real time. Let's refer to this job as SQL Streaming Crime Analyzer.

The high-level architecture of our job

The high-level architecture of our SQL Streaming Crime Analyzer will essentially consist of the following three components:

  • Crime producer: This is a producer that will randomly read the crime records from the file and push the data to a socket. This is same crime record file which...