Key takeaways for IT leaders and MSPs
Data science teams want the agility of containers — reproducible environments, fast CI/CD, and scale — while IT and MSPs are left holding the bill and the risk. The operational problem is not the containers themselves but persistent data: large, growing datasets, model artifacts, and experiment histories that don’t fit the ephemeral model. Left unmanaged, containerized pipelines create storage sprawl, unpredictable I/O bottlenecks, expensive cloud egress, and compliance gaps that blow budgets and audit windows.
Traditional storage approaches—SANs or ad-hoc NFS exports bolted onto Kubernetes, or wholesale lift-and-shift to public cloud block storage—fail because they treat data as an afterthought. They don’t expose lifecycle controls, granular QoS, or policy-based retention that data science workflows need, and they compound operational complexity for MSPs managing many tenants. The strategic shift is toward intelligent data platforms like STORViX that present a single, policy-driven storage layer to Docker and Kubernetes: CSI-compatible, performance-tunable, and designed to manage lifecycle, compliance, and cost across on-prem and hybrid deployments.
Do you have more questions regarding this topic?
Fill in the form, and we will try to help solving it.
