Not long ago I covered the topic of Big Data adoption in the enterprise. In it, I described how Serengeti enables enterprise to respond to common Hadoop implementation challenges resulting from the lack of usable enterprise-grade tools and the shortage of infrastructure deployment skills.
With the latest release of open source Project Serengeti, VMware continues on its mission to deliver the easiest and most reliable virtualized Big Data platform. One of the most unique attributes of Serengeti Hadoop deployment is that it can easily coexist with other workloads on an existent infrastructure.
Serengeti-deployed Hadoop clusters can also be configured in either local or shared, scale-out data storage architecture. This storage layer can even be shared across multiple HDFS-based analytical workloads. And, in the future, this could potentially be extended to other, non-HDFS-based data engines.
The elasticity of underlining vSphere virtualization platform, helps Serengeti to achieve new levels of efficiency. This architecture enables organizations to share the existing infrastructure with Big Data analytical workloads to deliver optimal storage capacity and performance. Continue reading