Exploiting Data Parallelism for R Scalability

Scaling solutions for large volume data can often be challenging. While some solutions require complex algorithm modifications to achieve parallelism and scalability, others can take advantage of more immediate data parallelism. The concept of data parallelism is often called out as addressing those “embarrassingly parallel” solutions - “embarrassing” because they’re so easy. A prime example is scoring data with a machine learning model. However, even with its conceptual simplicity, achieving a robust implementation can make production deployment more complex. Having ready-made and well-integrated infrastructure to support data parallelism can greatly reduce development overhead while improving the likelihood of project success.

Learn about data parallelism provided with Oracle Database through Oracle Machine Learning for R (OML4R). Define an R function, store it - and any related R objects - in the database, and have the database environment spawn and manage multiple R engines to enable scalability and performance. We’ll also demonstrate OML4R embedded R execution, which supports this important capability.