Cut costs and increase resource utilization with granular, dynamic allocation.
Get insights by abstracting IT complexity with policy-based, workload-aware resource management.
Ensure maximum availability and security of shared services with intelligent application and data lifecycle management.
Improve time to results through efficient resource scheduling and shared infrastructure.
Maximize usage of resources and eliminate resource silos of resources that would otherwise each be tied to multiple instances and different versions of Spark and other applications.
Utilize the included Spark distribution to make the framework simple to deploy both for exploratory projects and in production environments.