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 input sources

Storm works well with a variety of input data sources. Consider the following examples:

  • Kafka

  • RabbitMQ

  • Kinesis

Storm is actually a consumer and process of the data. It has to be coupled with some data source. Most of the time, data sources are connected devices that generate streaming data, for example:

  • Sensor data

  • Traffic signal data

  • Data from stock exchanges

  • Data from production lines

The list can be virtually endless and so would be the use cases that can be served with the Storm-based solutions. But in the essence of designing cohesive but low coupling systems, it's very important that we keep the source and computation lightly coupled. It's highly advisable that we use a queue or broker service to integrate the streaming data source with Storm's computation unit. The following diagram quickly captures the basic flow for any Storm-based streaming application, where the data is collated from the source and ingested into Storm:

The data is consumed, parsed, processed, and dumped...