Book Image

Learning Spark SQL

By : Aurobindo Sarkar
Book Image

Learning Spark SQL

By: Aurobindo Sarkar

Overview of this book

In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Understanding text analysis applications


The inherent nature of language and writing to problems of high dimensionality while analyzing documents. Hence, some of the most widely used textual methods rely on the critical assumption of independence, where the order and direct context of a word are not important. Methods, where word sequence is ignored, are typically labeled as "bag-of-words" techniques.

Textual analysis  is a lot more imprecise compared to quantitative analysis. Textual data requires an additional step of translating the text into quantitative measures, which are then used as inputs for various text-based analytics or ML methods. Many of these methods are based on deconstructing a document into a term-document matrix consisting of rows of words and columns of word counts. 

In applications using a bag of words, the approach to normalizing the word counts is important as the raw counts directly dependent on the document length. A simple use of proportions can  this problem, however...