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

Converting RDDs to DataFrames

In this section, we will discuss the strategies exposed by Spark SQL for transforming existing RDDs into DataFrames.

In today's enterprise world, data analysis requires the usage of more than one tool or technology. There could be scenarios where we want the Spark batch to initially load and process the data for a few insights and at the same we also want Spark SQL to process the same data to get the rest of the insights. In these kinds of scenarios, data would be loaded only once, either by a Spark batch or Spark SQL, and then it will be further processed by other Spark extensions. We need to consider that loading the data twice will be a waste of memory and time.

In order to solve this problem, Spark SQL (DataFrames) provides the interoperability with Spark batches (RDD). In short, Spark SQL provides APIs that can convert an RDD into a DataFrame and it can be used for data analysis.

Spark SQL provides two different processes for converting an existing RDD into...