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

Storm persistence

Now that we know Storm and its internals very well, let's wire in persistence to Storm. Well we have done all the computation and code, so now it's very important to store the computed results or intermediate references into a database or some persistence store. You have the choice of writing your own JDBC bolts or you can actually use the implementation provided for using Storm persistence.

Let's start with writing our own JDBC persistence first. Once we have touched upon the nitty gritty, then we can look at and appreciate what Storm provides for. Let's say we are setting up software system at toll gates that could monitor the emission rates of vehicles and track the details of the vehicles that are running at emissions beyond the prescribed limit.

Here, all the vehicle details and their emissions are captured in the file that is being read by the file reader spout. The spout then reads the record and feeds it into the topology where it is consumed by the parser bolt, which...