Oracle R Enterprise Essentials TrainingNow Available
Oracle has adopted R as a language and environment to support statisticians, data analysts and data scientists in performing statistical data analysis and advanced analytics, as well as generating sophisticated graphics.
Oracle R Enterprise (ORE) is a component of the Oracle Database Analytics Option. ORE makes the open source R statistical programming language and environment ready for the enterprise and big data.
Designed for problems involving large amounts of data - Oracle R Enterprise integrates R with Oracle Database
- Using ORE, R users can run R commands and scripts for statistical and graphical analyses on data stored in Oracle Database or outside the database.
- In both cases, users can leverage the power and scalability of Oracle Database to overcome the memory constraints of the base R environment.
- R users can develop, refine and deploy R scripts that leverage parallelism in the database to automate and vastly improve performance of the analytic process.
Because it runs as an embedded component of Oracle Database, ORE can run any R package, either by function pushdown or via embedded R scripts, while the database automatically manages the spawned R engines that process the data.
Oracle R Enterprise Essentials Training will teach you how to access the power of Oracle Database for enhanced R programming and analysis for big data opportunities.
What You Will Learn
Expert Oracle University instructors will guide you through the process of adding the power of ORE to your existing R technical portfolio. As you will learn, ORE transparently leverages the massive scalability of the database for the big data analysis problems.
In this course, you learn how to interact with database data using the R language and R data using ORE functions. In addition, you will develop the knowledge and skills necessary to use Oracle Database for predictive analysis.
Finally, you'll learn how to leverage the database server machine for executing R scripts from SQL and R, both individually and in a data-parallel and task-parallel manner.