Oracle R Connector for Hadoop (ORCH), a collection of R packages that enables Big Data analytics using HDFS, Hive, and Oracle Database from a local R environment, continues to make advancements. ORCH 2.1.0 is now available, providing a flexible framework while remarkably improving performance and adding new analytics based on the ORCH framework.
Previous releases enabled users to write MapReduce tasks in the R language and run them in HDFS. The API was then expanded to include support for Hive data sources, providing easy access to Hive data from R, leveraging the same transparency interface as found in Oracle R Enterprise. ORCH HAL was included to enable portability and compatibility of ORCH with any Cloudera's Hadoop distribution starting
from version 3.x up to 4.3.
In this release, new analytic functions that work in parallel, distributed mode and execute on the Hadoop cluster, include:
- Covariance and Correlation matrix computation
- Principal Component Analysis
- K-means clustering
- Linear regression
- Single layer feed forward neural networks for linear regression
- Matrix completion using low rank matrix factorization
- Non negative matrix factorization
- Sampling
- Predict methods
ORCH 2.1.0 also adds support for categorical variables, keyless mapReduce output, and many other improvements that contribute to overall performance enhancements.
You can find further details of the updates here, a technical reference here, and download Oracle R Connector for Hadoop here.