Book Image

Spark for Data Science

By : Srinivas Duvvuri, Bikramaditya Singhal
Book Image

Spark for Data Science

By: Srinivas Duvvuri, Bikramaditya Singhal

Overview of this book

This is the era of Big Data. The words ‘Big Data’ implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects.
Table of Contents (18 chapters)
Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Continuous applications


We discussed how unified data access is empowered by Spark. It lets you process data in a myriad of ways to build end-to-end continuous applications by enabling various analytic workloads, such as ETL processing, ad hoc queries, online machine learning modeling, or to generate necessary reports... all of this in a unified way by letting you work on both static as well as streaming data using a high-level, SQL-like API. In this way, Structured Streaming has substantially simplified the development and maintenance of real-time, continuous applications.

Courtesy: Databricks