SAN DIEGO, Calif., June 26 – Teradata, the analytic data platforms, marketing applications, and services company, today introduced Teradata Aster R, which extends the power of open source R analytics by lifting the memory and processing limitations. Teradata Aster R offers the R analyst an enterprise-ready business analytics solution that is massively scalable, reliable, and easy-to-use.
R analysts are challenged as they try to gain the maximum benefit from R when it is deployed on single server and only runs in-memory. The single server, in-memory environment restricts the amount of data that can be processed in-memory and can lead to slow performance of complex analytics. Teradata lifts the processing and memory limitations by offering parallel, in-database execution for R analytics. Executing R in-database allows for high-speed processing of massive quantities of data to meet the analytic needs of the organization. In addition, Teradata enables R analysts to access and integrate detailed data from multiple sources, and deploy a wider range of analytics for enhanced results.
R is an open source statistical language and software for data miners and data scientists. A Rexer Analytics survey reports that the number of data miners using R is growing; 70 percent of respondents reported they now use R.
“Teradata Aster R delivers the power of R analytics to the enterprise,” said Scott Gnau, president, Teradata Labs. “To support R analysts, Teradata offers familiar R language and tools, massive processing power, and a rich set of analytics. In addition, analysts have access to an immense volume of integrated data from multiple sources.”
Teradata Aster R leverages a high-performance compute platform with all the benefits of security, data management, and an ensemble of analytics. Three key components of the new solution are highlighted below.
Teradata Aster R Library – This library includes more than 100 pre-built R functions that run in parallel across all data, hiding the complexity of parallel processing. Analysts no longer need to spend days coding a parallel algorithm. They have immediate access to parallel R functions covering a range of tasks from data management, access, exploration, and manipulation, to machine learning algorithms. In addition, Teradata has augmented open source R capabilities with high-powered analytics including the patented Teradata Aster nPath.
Teradata Aster R Parallel Constructor – This component of Teradata Aster R allows analysts to build their own parallel analytics with more than 5,500 R analytic packages, or any new analytic functions developed in the open source community.
Teradata Aster SNAP Framework Integration – The open source R engine has been integrated into the Teradata Aster SNAP Framework, which enables multiple analytic engines and file stores to be seamlessly “snapped” together based on analysts’ tailored discovery needs. This seamless integration of multiple analytic capabilities enhances analytic power. Data scientists can delve deeply into data with multiple analytical capabilities such as graph, MapReduce, text, statistical, time series, and R analytics – from a single program. Teradata has augmented the Aster R library with additional high-powered graph analytic modules to support the previously announced Teradata Aster SQL-GR, a graph-processing engine. Data scientists will be able to use graph analytics for social network analytics, fraud detection, and belief propagation, a specialized algorithm for performing probability modeling.
“The rapid adoption of R and its proven value means that organizations looking to drive new revenue-generating insights should make R a part of their predictive analytics strategy,” James Taylor, chief executive officer, Decision Management Solutions. “Organizations adopting R should look to vendors with a solid plan for supporting R and providing scalable implementations.”
R Analytics Use Case – A streaming online movie provider must predict future revenue and take action to stop the potential attrition of profitable consumers, while improving their viewing experience. The movie provider assigns the task to an R analyst to help the enterprise understand customers’ buying preferences and service needs. However, the R analyst is unable to access and integrate all the necessary data required due to memory and processing limitations. If the analysis was run in a single server, in-memory environment it would likely not be completed in time to respond to the needs of the business user.
The addition of Teradata Aster R enables the analyst to overcome the memory and processing limitations. The analyst can orchestrate the entire analytic workflow through the R console. An in-database high-speed analysis can easily be performed on all required data including movie metadata, customer searches, viewing history, and account data from Apache Hadoop and Teradata. With these insights, the online movie provider can make decisions to serve customers and prevent attrition.