Map/Reduce for Parallel Data Analysis

Without a doubt, data analytics have a powerful new tool with the "map/reduce" development model, which has recently surged in popularity as open source solutions such as Hadoop have helped raise awareness.

You may be surprised to learn that the map/reduce pattern dates back to pioneering work in the 1980s which originally demonstrated the power of data parallel computing. Having proven its value to accelerate "time to insight," map/reduce takes many forms and is now being offered in several competing frameworks.

If you are interested in adopting map/reduce within your organization, why not choose the easiest and best performing solution? ScaleOut StateServer’s in-memory data grid offers important advantages, such as industry-leading map/reduce performance and an extremely easy to use programming model that minimizes development time.

Here's how ScaleOut map/reduce can give your data analysis the ideal map/reduce framework:

Industry-Leading Performance

Ease of Use

  • ScaleOut StateServer's object-oriented framework makes it easy to access data and write map/reduce methods. There's no need to complicate your code by accessing file-based data (as is required by other approaches, such as Hadoop).
  • Map/reduce methods are written as if they are to be run on a single machine. This means that they are easier to write than other approaches, thanks to ScaleOut’s language integration with C#, Java, and C.
  • Language integrated query makes it a snap to specify the data you need to analyze.
  • No gurus are needed! Because of ScaleOut StateServer's automatic parallelism, no low-level or parallel programming skills are needed.

Keep What You Have

  • ScaleOut StateServer's data grid acts as an intelligent NoSQL layer on top of your existing relational database. This preserves your existing investment in long term data storage.
  • ScaleOut StateServer runs natively on all major platforms and interoperates across platforms and languages.
  • ScaleOut StateServer can seamlessly migrate your on-premise data grid to and from the cloud.

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