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 internals

The moment people start talking about Storm, a few key aspects of this framework stand apart:

  • Storm parallelism

  • Storm internal message processing

Now, let's pick each of these attributes and figure out how Storm is able to deliver these capabilities.

Storm parallelism

If we want to enlist the processes that thrive within a Storm cluster, the following are key components to be tracked:

  • Worker process: These are the processes executing on the supervisor node and process a subset of the topology. Each worker process executes in its own JVM. The number of workers allocated to a topology can be specified in the topology builder template and is applicable at the time of topology submission.

  • Executors: These are the threads that are spawned within the worker processes for execution of a bolt or spout. Each executor can run multiple tasks, but being a single thread, these tasks on the executor are performed sequentially. The number of executors is specified while wiring in the bolts and...