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With Myriad, these organizations can deploy, manage, and monitor a single cluster that supports both Docker-based microservices deployed via Mesos frameworks like Marathon, as well as YARN-based processing applications like MapReduce and Spark.  All applications are fully isolated using Linux containers, ensuring that analytics workloads don’t interfere with operational applications or vise versa.  With fine-grained scaling, analytics workloads can consume large amounts of available resources when they need them, releasing them back to the shared pool when they are not.  In addition, distributed, shared data services can be provisioned on the shared cluster, eliminating data movement between applications and analytics.

Hadoop Multi-Tenancy

As organizations become more reliant on data processing technologies like Hadoop, it is common to encounter “cluster-sprawl” situations, where several different clusters are deployed to support different business groups or different lifecycle stages, such as dev, test, and prod, each running a different version.  Each new cluster requires new servers to be purchased and maintained, and large amounts of data to be copied over to support the new use case.  

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