**Revolutions**, and kindly contributed to R-bloggers)

Today, Teradata announced the new Teradata Database 14.10 and with it some exciting news for R programmers: the first next-generation in-database R analytics that are fully parallel and scalable. In conjunction with Revolution R Enterprise, R users will soon be able to use the power of the Teradata Database as a massively-parallel R platform, and use the parallel-external memory algorithms of Revolution R Enterprise ScaleR for advanced data processing and statistical modeling with big data.

By installing the forthcoming Revolution R Enterprise 7 in-database with Teradata 14.10, R users can expect:

*Faster Results*:

In-database operation brings computational parallelism that dramatically

accelerates the delivery of results from your data, and eliminates the

need to move data to a middle tier for analysis.*Reduced Latency by*: Running In-Database has dramatic effects

eliminating Data Movement

on big data analytics by eliminating the need to move data to analytics

servers before initiating exploration, modeling or scoring.*Expanded Capabilities*:

The computational performance of Revolution R Enterprise scales

linearly with system size, enabling developers to run more sophisticated

and more numerous analytic models on larger sets of your company’s data.

Transparent in-database execution reduces dependence on IT staff to move

data, freeing developers to pursue more forward-thinking projects,

including developing applications that enhance big data discovery.*Reduced Costs and Risks*: Accelerating analytics

with in-database processing reduces the need for additional data marts. R

is widely known and taught, broadening the available talent pool, reducing

training burdens, shortening ramp-up time, and cutting project cost. Based on

open source R, Revolution R Enterprise typically costs less than half as much

as other solutions.

The highest possible performance is achieved

because Revolution R Enterprise 7 for Teradata includes a library of Parallel

External Memory Algorithms (PEMAs). PEMAs are pre-built, extended-memory,

parallelized versions of the most common statistical and predictive analytics

algorithms and run directly in parallel on the Teradata nodes. Revolution R Enteprise includes PEMAs for data processing, data sampling, descriptive statistics, statistical tests, data visualization, simulation, machine learning and predictive models. All are accessible from easy-to-use R functions, and all use the parallel processing power of the Teradata database, without the need to move data anywhere.

Contact Revolution Analytics for more information about Revolution R Enterprise 7 for Teradata, which will be available later this year.

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