Revolution R Enterprise became SQL Server R Services (this is the in-database version of Microsoft R Server for the Windows operating system on SQL Server) and Microsoft R Server two products, which were both already available in Revolution R Enterprise.
Along with this acquisition, two versions of the R Engine came together: Revolution R Open (abbr.: RRO) for community and Revolution R Enterprise (abbr.: RRE) for commercial purposes.Īfter the acquisition, Microsoft kept Revolution R Open almost the same, and it became Microsoft R Open. Revolution analytics was acquired by Microsoft in April 2015. In the end, this R environment is built to move faster and better respond to change (in terms of faster preparing new data models, deploying and utilizing machine learning algorithms). The use of R will grow beyond a small database and help bring data in cloud as well as data on-premises closer together. The existing infrastructure (SQL Server databases, roles, access, security) also help operationalize the results as R in SQL Server is built to work on an enterprise scale and can cope with large volumes of data. This ability to offload this work represents also increase in productivity, as the data scientist can now focus on core analysis, shortening the time to deploy the prediction models, and facilitating real-time analytics. With R integrated in SQL Server environment, knowledge of data science can now be shared easier and faster as it moves from data scientists to data engineers and data stewards. Microsoft have stressed and overcome some of the major problems. R Integrationīefore SQL Server R Integration, many users, as well as many organizations, have had different challenges in terms of overcoming barriers for successful data analysis. This mean the integration of R into SQL Server makes analysis on larger datasets, real time OLTP analysis and any kind of big data analysis (document system such as Hadoop, Teradata and others) much easier than ever before. With R Services available in SQL Server 2016, Microsoft is making the R language available for more flexible data analysis, making sharing data insights much easier, and overcoming memory (RAM) limitations. It is a free software environment, is cross-platform (UNIX, Windows, MacOS), with most of the libraries and package development driven by community efforts. R is a statistical programming language, mainly used for statistical computing and analysis, data mining and machine learning, and it is a very strong tool for graphics and data visualizations.
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