Keywords: Big Data, Machine Learning, Scalability, High Perforance Computing, Graph Analytics
Webpages:
https://oracle.com/goto/R Big Data garners much attention, but how can enterprises extract value from data as found in the growing corporate
data lakes or
data reservoirs. Extracting value from big data requires high performance and scalable tools – both in hardware and software. Increasingly, enterprises take on massive machine learning and graph analytics projects, where the goal is to build models and analyze graphs involving multi-billion row tables or to partition analyses into thousands or even millions of components.
Data scientists need to address use cases that range from modeling individual customer behavior to understanding aggregate behavior, or exploring centrality of nodes within a graph to monitoring sensors from the Internet of Things for anomalous behavior. While
R is cited as the most used statistical language, limitations of scalability and performance often restrict its use for big data. In this talk, we present architectural elements enabling high performance and scalability, highlighting scenarios both on Hadoop/Spark and database platforms using
R. We illustrate how
Oracle Advanced Analytics’ Oracle R Enterprise component and
Oracle R Advanced Analytics for Hadoop enable using R on big data, achieving both scalability and performance.
