data lifecycles Different access profiles Expects different SLA Different backup policies Different versions Ops Maintenance windows are a drag Fair use is hard to impossible Upgrades affect all tenants Data isolation is not complete KibanaS VS
split?(by volume, tenant, use case, access profile) ‒ When one tenant gets its own cluster, then others will want one too • You now have multiple clusters to manage ‒ Different Elasticsearch versions ‒ Different support SLAs ‒ Different HW? Same HW? How do you isolate resources? ‒ What about backups? ‒ What about security? ‒ And how do you even track and monitor all of this?
on-premise, in your private cloud … or wherever you want Leverages the same technology used in Elastic Cloud Automates frequent tasks such as snapshot/restore, upgrade and scale
As a backing service for PaaS / SaaS • Examples: ‒ Service Brokers for CloudFoundry and OpenShift ‒ Cluster per customer in a SaaS app • Easily create a cluster per user / tenant • Maintain isolation between users and tenants