Synopsis
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
"synopsis" may belong to another edition of this title.
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. Kevin builds open source libraries for machine learning and model deployment. He has held data science positions in various industries including insurance where he was a credentialed actuary. Kevin is the creator of mlflow, mleap, sparkxgb among various R packages. He is also an amateur mixologist and sommelier. Edgar Ruiz has a background in deploying enterprise reporting and business intelligence solutions. He is the author of multiple articles and blog posts sharing analytics insights and server infrastructure for data science. Edgar is the author and administrator of the db.rstudio.com web site, and the current administrator of the sparklyr web site. He's also the co-author of the dbplyr package, and creator of the dbplot, tidypredict and the modeldb package.
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Paperback. Condition: New. 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 RCreate statistical models to extract information and predict outcomes; automate the process in production-ready workflowsPerform analysis and modeling across many machines using distributed computing techniquesUse large-scale data from multiple sources and different formats with ease from within SparkLearn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scaleDive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions. Seller Inventory # LU-9781492046370