Apache Spark has become the most currently active project in the Hadoop ecosystem in terms of the number of contributors by the end of 2015. Having started as a research project at UC Berkeley AMPLAB in 2009, Spark is still relatively young when compared to projects such as Apache Hadoop and is still in active development. There were three releases in the year 2015, from 1.3 through 1.5, packed with features such as DataFrames API, SparkR, and Project Tungsten respectively. Version 1.6 was released in early 2016 and included the new Dataset API and expansion of data science functionality. Spark 2.0 was released in July 2016, and this being a major release has a lot of new features and enhancements that deserve a section of their own.
Spark for Data Science
By :
Spark for Data Science
By:
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
Free Chapter
Big Data and Data Science – An Introduction
The Spark Programming Model
Introduction to DataFrames
Unified Data Access
Data Analysis on Spark
Machine Learning
Extending Spark with SparkR
Analyzing Unstructured Data
Visualizing Big Data
Putting It All Together
Building Data Science Applications
Customer Reviews