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Big Analytics For Hadoop and EDWs

Run analytics “Inside” Teradata, Cloudera and Hortonworks platforms to transparently scale your analytical capability

Parallel Performance Without Parallel Complexity

Big Data drives optimum value when it yields fast insights. Adopting MPP data warehouses or Hadoop clusters alone to store Big Data isn’t enough. As data grows, so does complexity and computational workload analyzing Big Data.

Big Data Analytics Cripples Legacy Tools

If you have searched to find a way to easily scale analytics for EDWs and Hadoop using your legacy analytical tools you’ve likely encountered some of these crippling issues:

  • Complexity: Pioneering users have found that writing analytics in MapReduce or in SQL-based tools is a tedious, error-prone process.
  • Inflexibility: Binding R scripts to SQL-based algorithms restricts functionality and constrains performance.
  • Vendor Dependence: Using R-to-MapReduce translation solutions or R wrappers for SQL algorithms locks your analytical applications to specific platforms complicating any future platform evolutions.

Revolution Provides Analytical Scale That Is Also Easy to Use

Revolution R Enterprise transparently runs R analytics inside Hadoop and Teradata EDWs, providing your team with:

Speed:

  • Performance: Transparent distributed computing of analytical algorithms
  • Efficiency: Analytics without moving or duplicating Big Data

Scale:

  • Scalability: By running on all nodes of the EDW or Hadoop platform, analytics are easily and transparently accelerated.
  • Transparency: Moving analytics from workstations to Hadoop or Teradata does not require program changes.

Capability:

  • Functionality: Revolution R Enterprise supplies a rich set of fast data mining, machine learning, predictive modeling, data scoring and statistical tests for users of Hadoop and the Teradata Database.
  • Simplicity: Analytics are developed using GUI-based development tools, with no need to learn Java or complex SQL.
  • Portability: Our Write Once Deploy Anywhere portability leads the analytics industry. Write your analytics for a laptop, run it on an EDW and a Hadoop cluster in the same day.
  • Compatibility: Revolution R Enterprise algorithms consume and produce data that is compatible with other applications in Teradata Database, Cloudera Hadoop and Hortonworks Hadoop.
  • Manageability: When running in Teradata and Hadoop, analytics processes are easily managed using the standard administration facilities of each platform.

Film is Meant to be Exposed. Not Sensitive Data

The most vulnerable point for your data, where it’s most accessible to prying eyes, occurs when is moved or duplicated. Revolution R Enterprise analyzes data inside the Teradata Database and Hadoop, without moving data or creating duplication, reducing vulnerabilities. In return, your analytics run faster, sensitive data is more secure and data governance is simpler.

We Hide the Truth. On Purpose!

Writing your own parallel computing functions, whether you write them in R, Java, Python or C++ is hard work. Manually integrating them into Teradata or Hadoop is harder still, and production hardening them is out of reach of many.

We accelerate your analytics on Teradata and on Hadoop, while hiding the truth of parallel computing: its complexity. For analytics users, support for Hadoop or in Teradata Database in Revolution R Enterprise accelerates analytics means you can maximize performance without having to build your own parallelized algorithms and applications. R developers can focus on the business of finding and exploiting insights in from data rather than on the development complexities of building in-Hadoop and in-Database applications manually.

Big Data Platforms With Revolution R Enterprise Deliver

Revolution R Enterprise provides the analytical horsepower to run Big Data Big Analytics available from Teradata Database, Cloudera Hadoop and Hortonworks Hadoop. Your users will be able to generate richer business insight for users across the organization easily and efficiently while embracing larger data sets.