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

Chapter 6. Getting Acquainted with Spark

It is all about data! Isn't it?

One of the most critical objectives of most enterprises is to churn/analyze and mashup the variety of data received from different channels, such as CRM, portals, and so on, and uncover the truth that can help them formulate business/marketing strategies, informed decisions, predictions, recommendations, and so on. Now what matters is how efficiently, effectively, and quickly you can uncover the hidden patterns in the data.

The sooner you can, the better it will be!

Distributed computing ( or the paradigm of parallel computing/processing played a pivotal role in achieving the key objectives of enterprises. Distributed computing helped enterprises to process large datasets on multiple nodes that were connected to each other, which may be geographically distributed. All these nodes interact with each other and work toward achieving the common goal.

One of the most popular...