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

Working with Parquet

In this section, we will discuss and talk about various operations provided by Spark SQL for working with Parquet data formats with appropriate examples.

Parquet is one of popular columnar data storage format for storing the structured data. Parquet leverages the record shredding and assembly algorithm ( as described in the Dremel paper ( Parquet supports efficient compression and encoding schemes which is better than just simple flattening of structured tables. Refer to for more information on the Parquet data format.

The DataFrame API of Spark SQL provides convenience operations for writing and reading data in the Parquet format. We can persist Parquet tables as temporary tables within Spark SQL and perform all other operations provided by the DataFrame API for data manipulation or analysis.

Let's see the example for writing/reading Parquet data formats and then we will...