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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 and 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 vice 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.

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In case of a version migration (for example, upgrading only one of two Hadoop clusters), this model means that two logical Hadoop clusters of different versions can be deployed side by side on top of the same shared data.  Users can migrate workloads from old versions to new versions gradually, add resources to the new cluster, and take resources away from the old cluster.  After all workloads are moved over, the old cluster can be decommissioned.

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