Revolution R Enterprise is the fastest, most cost effective enterprise-class big data big analytics platform available today. Supporting a variety of big data statistics, predictive modeling and machine learning capabilities, Revolution R Enterprise is also 100% R. Revolution R Enterprise provides users with the best of both – cost-effective and fast big data analytics that are fully compatible with the R language, the de facto standard for modern analytics users.
Offering high-performance, scalable, enterprise-capable analytics, Revolution R Enterprise supports a variety of analytical capabilities including exploratory data analysis, model building and model deployment.
Revolution R Enterprise is available in Workstation and Server editions. The current Revolution R Enterprise editions is distributed with Open Source R (version 3.1.1), is 100% compatible with R scripts, functions and CRAN packages, and includes phone and online technical support.
Revolution R Enterprise Workstation is for single users on Windows and Linux desktop/laptop computers with up to 8 cores.
Revolution R Enterprise Server is for unlimited users, and is supported on servers, clusters, grids, Hadoop, and enterprise data warehouse appliances.
R Language Engine
|Multi-core processing (Intel® Math Kernel Library)||Included||Supported||Supported|
|NEW! Reproducible R Toolkit||Included||Supported||Supported|
|ParallelR: Parallel Programming Toolkit||Supported||Supported|
|RHadoop: R interface to Hadoop MapReduce||Supported||Supported|
|NEw! DeployR Open: Web Services API||Supported||Supported|
|RRE DeployR – Scalable & Secure Deployment||Licensed & Supported|
|RRE ScaleR – Big Data Toolkit and PEMAs for R||Licensed & Supported|
|RRE DistributedR – EDW, Grids, Hadoop||Licensed & Supported|
|AdviseR Technical Support||Included||Included|
|NEW! Open Source Assurance||Included||Included|
Speed and Scale in Parallel Systems
Revolution R Enterprise scales and accelerates R, running R scripts in a high-performance, parallel architecture that supports systems from workstations to clusters and grids including Hadoop and enterprise data warehouses.
Revolution R Enterprise accelerates traditional statistical analysis using big data computation and data management techniques. With Revolution R Enterprise, R users can explore, model, and predict at scale.
Deploy advanced, R-based analytics inside of the leading Hadoop and EDW platforms, Revolution R Enterprise scales analytics to even greater levels of data and computational scale.
Ensuring Your Analytics Success
Revolution R Enterprise delivers the assurance that you need to deploy advanced analytics confidently within your mission critical applications. Securely integrate your results with your enterprise applications. Build dashboards, custom reporting and provide analytics results to operations, financial and marketing systems. Create leverage across your organization with the insights you need to create better performance for every department.
Revolution R Enterprise excels at unlocking knowledge, including trends, behaviors, predictions and even outliers inside enterprise data. DeployR is designed to deliver that knowledge securely and at scale to whomever needs it: inside or outside the enterprise. By providing a seamless, secure, data bridge between analytics solutions and enterprise software, DeployR solves a key integration problem faced by businesses adopting R based analytics alongside existing IT infrastructure.
Data scientists typically focus on developing analytics solutions using software tools on their workbench. Using DeployR, these solutions can now be leveraged by your business far beyond those workbenches. The DeployR server can expose any R based analytics solution as a secure web service. These web services can then be integrated seamlessly into existing IT infrastructure or become part of new IT solutions.
- Maximize the impact of your R data scientists
- Let data scientists remain focused on creating the R code necessary to drive your analytics solutions.
- Just one click in the DeployR Console can turn any R code created by a data scientist into a secure web service.
- DeployR powered secure web services can immediately be integrated by system integrators and application developers into Web, desktop and mobile applications.
- Even Excel spreadsheets and BI dashboards can be supercharged to deliver powerful analytics solutions using DeployR secure web services.
Deliver our R Analytics Securely Across the Enterprise
- Guarantee secure delivery of your analytics via DeployR web services.
- Secure web services integrate seamlessly with existing enterprise security solutions: Single Sign-On, LDAP, Active Directory, PAM, and Basic Authentication.
- Secure web services can enforce access privileges already defined by your IT department for existing enterprise users.
- Secure web services even have the capability to safely support anonymous users when needed.
Deliver your R Analytics at Scale
- DeployR offers the ability to expand and contract operating capacity to effectively scale for varying analytic workloads.
- DeployR supports sophisticated load balancing capabilities to efficiently distribute analytic workloads across available resources.
- DeployR powered secure web services can handle requests on demand or permit scheduling of analytics for background execution.
Simplify R Analytics Integration for your Software Development Teams
- By employing standards based technology, DeployR allows existing development teams to integrate R statistics, analytics and visualizations inside any IT solution.
- Development teams can reuse existing skills and software tools to integrate with DeployR powered secure web services.
- With DeployR developers can extend existing enterprise applications or build new ones to take full advantage of R based data analytics.
Simplify R Analytics Deployment for your IT Management Teams
- DeployR can be installed on both Linux and Microsoft Windows Server platforms.
- DeployR supports flexible deployment on-site, and in private, public and even hybrid cloud environments.
Transparent Parallelism Accelerates Big Data Analytics Easily
Revolution R Enterprise provides computational and data size scalability through ScaleR, a library of big data analytics algorithms. Revolution R Enterprise ScaleR provides data scientists with a range of R algorithms that provide transparent parallelization of computations and data analysis they can easily scale to Big Data. ScaleR brings big data analytics within reach without added complexity or the need to learn new languages or parallel programming. It includes a rich set of data preparation, statistics, predictive modeling, and machine learning algorithms that accelerate Big Data Big Analytics and support systems ranging from workstations and servers, clustered systems such as Hadoop, EDWs from Teradata or IBM or compute grids from Microsoft and IBM.
Comprehensive Big Data Analytics Algorithms in ScaleR
The following is a list of the big-data ready capabilities included with Revolution R Enterprise:
- Data import: ASCII, SAS, SPSS, ODBC, HDFS
- Variable creation
- Variable transformation and recoding
- Sort / Merge / Split
- Random Sampling
- Min / Max / Mean
- Median and Quantiles
- Standard Deviation / Variance
- Correlation / Covariance/ Sum of Squares cross-product matrix
- Cross-Tabulations and marginal summaries
- Aggregation by category
Data Visualization for Big Data
- Line Plot / Scatter Plot
- Lorenz Curve
- ROC Curve
- Tree Visualizer
- Chi-squared Test
- Fisher’s Exact Test
- Kendall’s Tau Rank Correlation Coefficient
- Risk Ratio and Odds Ratio on two-by-two objects
Parallelized Statistical Modeling Algorithms
- Linear Regression
- Logistic Regression
- Multiple Regression
- Generalized Linear Models with all multiple exponential distributions (including Tweedie distribution) and a variety of standard and user-defined link functions
- Stepwise Regression – Linear, GLM & Logistic
- Clustering using K-Means Clustering
- Predictions for fitted models
- PMML export
Classification and Machine Learning Capabilities:
- Decision Trees
- Ensemble modeling with Decision Trees (similar to Random Forests)
Transparent Parallelism Brings Fast Execution
ScaleR algorithms enable R developers to run R scripts on massive data sets at high speeds. In conjunction with DistributedR, ScaleR transparently distributes analytics computations across all available resources – threads, cores, processors and nodes.
No Additional Languages, No Parallel Software Development
ScaleR enables R developers to easily maximize compute capability without writing any distributed applications themselves. This has two advantages over other solutions:
- No Java, Python or other programming skills are needed to harness the power of massively parallel systems including Hadoop and Teradata EDWs.
- No Parallel Programming. R developers are provided with transparent parallelism, so that they aren’t slowed by the complexity of parallel program design. Parallelism is provided transparently within the Scale Algorithm set.
Available Parallel Platforms
Revolution R Enteprise DistributedR brings all these Big Data algorithms distributed computing parallel platforms. Use the computing power of servers, grids, databases and Hadoop — without the need to move the data anywhere. DistributedR is supported on the following platforms:
- Teradata EDWs
- Hadoop clusters
- IBM Platform LSF clusters of Linux servers.
No Memory Barriers
Revolution R Enterprise ScaleR algorithms are implemented as Parallel External Memory Algorithms (PEMAs). By managing available RAM and permanent storage together, PEMAs are able to analyze data well beyond the limits of available memory.
- Compute Faster: Most ScaleR PEMA are optimized to run faster than their open source equivalents on both small and large data sets.
- Unlimited Data Size: PEMAs process data in “chunks” – moving data into memory as needed, enabling the algorithm to operate on data that far exceeds available memory.
- Fast Parallel Computation: PEMAs divide work into smaller pieces, distributing them across available cores, and nodes to dramatically accelerate modeling and machine learning.
- Efficient Analysis of Distributed Data: Storage of data in MPP EDWs and Hadoop clusters is distributed across many nodes of the compute cluster. By analyzing local data using local compute resources, data movement and consolidation is eliminated, providing optimum efficiency and speeding computation.