Apache Spark is the next standard of open-source cluster-computing engine for processing big data. Many practical computing problems concern large graphs, like the Web graph and various social networks. The scale of these graphs – in some cases bill
Develop large-scale distributed data processing applications using Spark 2.0 in Scala and Python About This Book This book offers an easy introduction to the Spark framework published on the latest version of Apache Spark 2.0 It is aimed at beginner
Key Features Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenge
Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools which that are equally useful for application developers as well as data scientists. SparkR or “R on Spark” in the Spa
Mastering Spark for Data Science by Andrew Morgan English | 29 Mar. 2017 | ASIN: B01BWNXA82 | 560 Pages | AZW3 | 12.66 MB Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grad
Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark by Russell Jurney English | 7 Jun. 2017 | ASIN: B072MKL34K | 352 Pages | AZW3 | 5.91 MB Data science teams looking to turn research into useful analytics applications
Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists
Apache Spark for Data Science Cookbook The objective of this book is to get the audience the flavor of challenges in data science and addressing them with a variety of analytical tools on a distributed system such as Spark (apt for iterative algorit
The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-
