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

Big Data – a phenomenon

The phrase Big Data is not just a new buzzword, it's something that arrived slowly and captured the entire arena. The arrival of Hadoop and its alliance marked the end of the age for the long undefeated reign of traditional databases and warehouses.

Today, we have a humongous amount of data all around us, in each and every sector of society and the economy; talk about any industry, it's sitting and generating loads of data—for instance, manufacturing, automobiles, finance, the energy sector, consumers, transportation, security, IT, and networks. The advent of Big Data as a field/domain/concept/theory/idea has made it possible to store, process, and analyze these large pools of data to get intelligent insight, and perform informed and calculated decisions. These decisions are driving the recommendations, growth, planning, and projections in all segments of the economy and that's why Big Data has taken the world by storm.

If we look at the trends in the IT industry, there was an era when people were moving from manual computation to automated, computerized applications, then we ran into an era of enterprise level applications. This era gave birth to architectural flavors such as SAAS and PaaS. Now, we are into an era where we have a huge amount of data, which can be processed and analyzed in cost-effective ways. The world is moving towards open source to get the benefits of reduced license fees, data storage, and computation costs. It has really made it lucrative and affordable for all sectors and segments to harness the power of data. This is making Big Data synonymous with low cost, scalable, highly available, and reliable solutions that can churn huge amounts of data at incredible speed and generate intelligent insights.