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

Handling persistence in Spark

In this section, we will discuss how the persistence or caching is being handled in Spark. We will talk about various persistence and caching mechanisms provided by Spark along with their significance.

Persistence/caching is one the important components or features of Spark. Earlier, we talked about the computations/transformations are lazy in Spark and the actual computations do not take place unless any action is invoked on the RDD. Though this is a default behavior and provides fault tolerance, sometimes it also impacts the overall performance of the job, especially when we have common datasets that are leveraged and used across the computations.

Persistence/caching helps us in solving this problem by exposing the persist() or cache() operations in the RDD. The persist() or cache() operations store the computed partition of the invoking RDD in the memory of all nodes and reuses them in other actions on that dataset (or datasets derived from it). This enables...