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
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

The Big Data dimensional paradigm


To begin with, in simple terms, Big Data helps us deal with the three Vs: volume, velocity, and variety. Recently, two more Vs—veracity and value—were added to it, making it a five-dimensional paradigm:

  • Volume: This dimension refers to the amount of data. Look around you; huge amounts of data are being generated every second—it may be the e-mail you send, Twitter, Facebook, other social media, or it can just be all the videos, pictures, SMS, call records, or data from various devices and sensors. We have scaled up the data measuring metrics to terabytes, zettabytes and vronobytes—they are all humongous figures. Look at Facebook, it has around 10 billion messages each day; consolidated across all users, we have nearly 5 billion "likes" a day; and around 400 million photographs are uploaded each day. Data statistics, in terms of volume, are startling; all the data generated from the beginning of time to 2008 is kind of equivalent to what we generate in a day today, and I am sure soon it will be an hour. This volume aspect alone is making the traditional database unable to store and process this amount of data in a reasonable and useful time frame, though a Big Data stack can be employed to store, process, and compute amazingly large datasets in a cost-effective, distributed, and reliably efficient manner.

  • Velocity: This refers to the data generation speed, or the rate at which data is being generated. In today's world, where the volume of data has made a tremendous surge, this aspect is not lagging behind. We have loads of data because we are generating it so fast. Look at social media; things are circulated in seconds and they become viral, and the insight from social media is analyzed in milliseconds by stock traders and that can trigger lot of activity in terms of buying or selling. At target point of sale counters, it takes a few seconds for a credit card swipe and, within that, fraudulent transaction processing, payment, bookkeeping, and acknowledgement are all done. Big Data gives me power to analyze the data at tremendous speed.

  • Variety: This dimension tackles the fact that the data can be unstructured. In the traditional database world, and even before that, we were used to a very structured form of data that kind of neatly fitted into the tables. But today, more than 80 percent of data is unstructured; for example, photos, video clips, social media updates, data from a variety of sensors, voice recordings, and chat conversations. Big Data lets you store and process this unstructured data in a very structured manner; in fact, it embraces the variety.

  • Veracity: This is all about validity and the correctness of data. How accurate and usable is the data? Not everything out of millions and zillions of data records is corrected, accurate, and referable. That's what veracity actually is: how trustworthy the data is, and what the quality of data is. Two examples of data with veracity are Facebook and Twitter posts with nonstandard acronyms or typos. Big Data has brought to the table the ability to run analytics on this kind of data. One of the strong reasons for the volume of data is its veracity.

  • Value: As the name suggests, this is the value the data actually holds. Unarguably, it's the most important V or dimension of Big Data. The only motivation for going towards Big Data for the processing of super-large datasets is to derive some valuable insight from it; in the end, it's all about cost and benefits.