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

The architecture of Spark SQL

In this section, we will discuss the overall design, architecture, and various components of Spark SQL. This will help us to understand the varied features and capabilities of Spark SQL.

The emergence of Spark SQL

Storing data in relational structures such as Relational Database Management Systems (RDBMS) (such as Oracle, MySQL, and others) and leveraging SQL is a well-known and industry-wide standard for performing analysis over the data collected from various sources such as online portals, surveys, and so on.

It worked fine but only till the time when the data was limited and reasonable in size, that is, not more than a few GBs. As soon as it grew to TBs, it started giving nightmares where SQL queries would take hours, sometimes they would not even complete, and many a times crashed the whole system itself.

That's where Apache Hadoop ( was introduced as a distributed, scalable, fault tolerant, parallel, and batch processing...