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
- ScaleOut StateServer's in-memory data grids provide extremely fast data access for map/reduce. This avoids the overhead of staging data from disk and keeps the network from becoming a bottleneck.
- ScaleOut StateServer eliminates unnecessary data motion by load-balancing the distributed data grid and accessing data in place. This gives your map/reduce consistently fast data access.
- Automatic parallel speed-up takes full advantage of all servers, processors, and cores.
- Integrated, easy-to-use APIs enable on-demand analytics; there's no need to wait for batch jobs.
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 distributed 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.
Learn more!
- Take a closer look at ScaleOut StateServer Grid Computing Edition.
- Download a map/reduce case study that explains the advantages of ScaleOut StateServer.
- Download a free trial version of ScaleOut StateServer.
- Contact us for more information.
