Massive storage

Many other companies also require data lakes as secondary storage, mainly to store data in its raw form for analysis, real-time analytics, machine learning, and more. SDS is excellent for this type of storage, mainly because the maintenance required is minimal and also for the economic reasons that we discussed previously.

We have been talking mainly about how economic and scalable SDS is, but it is also important to mention the high flexibility that it brings to the table. SDS can be used for everything from archiving data and storing reach media to providing storage for virtual machines (VMs), as an endpoint for object storage in your private cloud, and even in containers. It can be deployed on any of the previously mentioned infrastructures. It can run on your public cloud of choice, in your current on-premises virtual infrastructure, and even in a Docker container or Kubernetes pod. In fact, it's so flexible that you can even integrate Kubernetes with GlusterFS using a RESTful management interface called heketi that dynamically provisions volumes every time you require persistent volumes for your pods.