revolution (software)

revolution (software)
Name	revolution (software)
Developer	Revolution Analytics
Released	0 2007
Programming language	R (programming language)
Operating system	Microsoft Windows, Linux, macOS
Genre	Statistical software, Integrated development environment
License	Proprietary software

revolution (software). Revolution, now known as Revolution R Enterprise, was a commercial distribution and integrated development environment for the R (programming language), developed by Revolution Analytics. It was designed to enhance the performance, scalability, and manageability of the open-source R (programming language) for enterprise and academic use, particularly in the fields of big data analytics and statistical computing. The software provided proprietary extensions and support, aiming to bridge the gap between the open-source R Project and the needs of large-scale commercial deployments.

Overview

Revolution was built upon the standard R (programming language) distribution from the R Project, maintaining full compatibility with its vast ecosystem of packages from repositories like CRAN. Its primary value proposition was adding high-performance computing capabilities, including multi-threaded BLAS and LAPACK libraries, and advanced data handling tools for large datasets. The company, Revolution Analytics, was founded by predictive analytics experts including Norman Nie and was later acquired by Microsoft in 2015, leading to its integration into the Microsoft Azure and Microsoft SQL Server platforms. The software targeted users in industries such as finance, pharmaceuticals, and academic research who required robust, supported statistical analysis solutions.

Features

Key features of Revolution included the RevoScaleR package, which enabled distributed and parallel computing on datasets exceeding RAM size, supporting data formats from sources like Teradata and Hadoop. It offered an integrated development environment called Revolution R Enterprise IDE, providing enhanced code editing, debugging, and visualization tools compared to the standard RStudio or R GUI. Performance was boosted through optimized Intel MKL libraries for linear algebra and the ability to deploy analytical models as services via DeployR. Additional proprietary packages facilitated easier data manipulation, connection to ODBC databases, and the generation of reports in formats like PDF and Microsoft Excel.

Development and history

The development of Revolution began in 2007 under Revolution Analytics, a company launched by Norman Nie, co-creator of the SPSS statistical package. Initial versions focused on providing a supported, enterprise-ready version of R (programming language), with major releases adding scalable data analysis functions. A significant milestone was the 2011 release of Revolution R Enterprise 6.0, which introduced the RevoScaleR big data analytics engine. Following the 2015 acquisition by Microsoft, the commercial Revolution product line was gradually phased out, with its core technologies integrated into Microsoft R Open and Microsoft R Server, which later evolved into Machine Learning Server and services within Microsoft Azure.

Applications and use cases

Revolution was deployed for large-scale statistical modeling and predictive analytics across various sectors. In finance, institutions like Bank of America used it for risk management and fraud detection on massive transaction datasets. Within the pharmaceutical industry, companies such as Pfizer applied it to clinical trial data analysis and genomics research. Academic researchers at universities like Stanford University utilized its high-performance capabilities for complex simulations in computational biology and social sciences. It also saw use in government agencies, including the United States Census Bureau, for processing and analyzing large demographic surveys.

Technical architecture

The architecture centered on the RevoScaleR package, which used an external memory algorithm design to process data in chunks from storage systems like HDFS or SQL Server, bypassing RAM limitations. It employed a parallel computing framework that could distribute workloads across multiple cores on a single machine or across nodes in a Hadoop or Spark cluster. The underlying R (programming language) interpreter was linked with performance-optimized libraries such as the Intel Math Kernel Library for numerical computations. Connectivity was provided through interfaces to ODBC, JDBC, and native drivers for databases including Oracle Database and SAP HANA.

Reception and impact

Revolution was generally well-received for bringing commercial-grade support and performance to the R (programming language) ecosystem, earning recognition from analysts like Gartner in its Magic Quadrant. It influenced the broader adoption of R (programming language) in enterprise settings, demonstrating that open-source statistical tools could meet the demands of Fortune 500 companies. The acquisition by Microsoft validated the strategic importance of R (programming language) and advanced analytics, shaping the development of Microsoft's data science platform. Its technology legacy continues within Microsoft Azure Machine Learning and contributed to the maturation of tools for big data statistical analysis.

Category:Statistical software Category:R (programming language) Category:Proprietary software Category:Data analysis software