R/Database: Using R at Scale on Database Data
Channel: R Consortium
Date: 09/12/2022

Speakers:
Mark Hornick, Senior Director, Oracle Machine Learning
Sherry LaMonica, Consulting MTS, Oracle Machine Learning

Many users, organizations, and enterprises rely on R as a powerful language and environment for statistical analysis, data science, and machine learning. Many of these same users work with data in – or extracted from – Oracle databases. 

As data volumes increase, moving data complicates the life of data professionals, like data engineers and data scientists, as well as solution developers and administrators. In this session, you’ll learn how to increase overall solution performance with R tightly integrated with Oracle databases. Oracle Machine Learning for R (OML4R: https://www.oracle.com/database/technologies/datawarehouse-bigdata/oml4r.html) leverages the database as a high-performance computing environment, enabling users to explore, transform, and analyze data faster and at scale, while allowing the use of familiar R syntax and semantics. In-database parallelized machine learning algorithms are exposed through a natural R interface, including the use of R Formula. R users can run user-defined R functions in database-environment spawned and managed R engines using R, SQL, and REST interfaces – even taking advantage of system-enabled data parallelism. User-defined R functions and other R objects can be stored directly in the database to facilitate ease of solution deployment while benefiting from database security – avoiding the use of flat files. This engaging session highlights multiple use cases with demonstrations. 

#Rstats #rprogramming #opensource 

Main Sections 

00:00 Introduction
2:54 Agenda
3:33 Background: R for databases 
8:43 R Packages for database access
13:22 Memory and scalability 
15:24 Leveraging parallelism
22:03 OML4R introduction 
26:00 Demonstration: OML4R data exploration and preparation, dplyr 
31:40 In-database algorithms 
34:13 Demonstration: OML4R modeling
37:14 Embedded execution 
39:14 Demonstration: OML4R embedded R execution
49:01 Using additional third-party packages with ODB/ADB
50:21 Oracle Machine Learning related components 
51:08 Summary and more information 

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