Mastering Spark With R by Javier Luraschi

£39.99
Out of stock
Product Ref. (e.g. ISBN, SKU)
9781492046370
Published:
18 Oct 2019
Format:
Paperback
Number of pages:
293 pages
Dimensions:
233x178x16mm
Weight:
0.520kgs
Author:
With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.

If you're like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.

Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions

About the Author: Javier is a software engineer with experience in technologies ranging from desktop, web, mobile and backend, to augmented reality and deep learning applications. He previously worked for Microsoft Research and SAP and holds a double degree in Mathematics and Software Engineering. He is the author of various R packages like sparklyr, cloudml, r2d3, mlflow, tfdeploy and kerasjs.

More Information
Number of pages 293

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