We are pleased to announce that Platform Computing 1.5 is now available. Compared to its predecessor, Platform MapReduce v1.0 released in late June, the newly released version brings a number of enhancements in the key functionalities the product delivers. Major improvements include the availability of the MapReduce application adapter technology, which allows users to execute their existing Hadoop applications without changing the code or recompile. Enhancements have also been made to the runtime layer so that mixed workloads can run on a same cluster simultaneously. In addition, the support of IBM GPFS in the data layer is perhaps the most compelling capability in this release because it delivers a powerful solution to users running Hadoop applications on GPFS instead of a designated file system.
For more on Platform MapReduce 1.5, please see: http://info.platform.com/rs/platform/images/Datasheet_PlatformMapReduce.pdf
So what does it all boil down to? Well, there are a number of immediate benefits with the new version:
- The support of mixed workloads running on the same cluster simultaneously improves resource utilization and drives shared services model for IT, therefore multiple business lines can share the same infrastructure and a centralized IT management
- Increased developer productivities and choices. With the application adapter technology offered in Platform MapReduce 1.5, developers can build their applications using their preferred MapReduce programming framework and run their code without making changes to the code or recompile, thus accelerating the application development cycle while eliminating vendor lock-in.
- The integration of IBM GPFS and Platform MapReduce 1.5 allows users to run MapReduce applications directly on the data stored in GPFS instead of moving the data to a designated file system before the application execution, which can be a very costly operation. In addition, the combined technologies deliver the best of both worlds to users running MapReduce applications.
- The unique capability of supporting different data input from output in Platform MapReduce 1.5 provides a more efficient approach to the ETL function as it eliminates the requirements for data staging at output, which can be a time consuming and expensive operation.
The past couple of months have been busy and exciting for us at Platform. We are seeing increased interest in the key functionalities offered in Platform MapReduce 1.5. As Hadoop / MapReduce continue to gain market traction, users will become more educated on these emerging technologies and many will begin to move their MapReduce applications from labs to true production environments. We believe Platform MapReduce will play a critical role in this transition by delivering a reliable, efficient and proven solution to users running MapReduce in production.