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

Understanding LMAX

One of the key aspects that attributes to the speed of Storm is the use of LMAX disruptor versus queues. We did touch upon this topic in one of the earlier chapters, but now we are going to dive deep into the same. To be able to appreciate the use of LMAX in Storm, we first need to get acquainted with LMAX as an exchange platform.

Just to reiterate what's been stated in one of the earlier chapters, this is how internal Storm communication happens:

  • Communication within different processes executing on the same worker (in a way, its inter-thread communication on a single Storm node); the Storm framework is designed to used LMAX disruptor

  • Communication between different workers across the node might be on the same node (here, ZeroMQ or Netty is used)

  • Communication between two topologies is attained by external and non-Storm mechanisms such as queues (for example, RabbitMQ, Kafka, and so on) or distributed caching mechanisms (for example, Hazelcast, Memcache, and so on)

LMAX is...