Key Takeaways
Why is storage becoming an infrastructure architecture problem, rather than just a capacity problem?
Data is now distributed across clouds, data centers, AI platforms, and recovery environments. That means organizations need to consider not only where data is stored, but how quickly, securely, and economically it can reach the applications and compute that need it. Network latency, throughput, predictability, security, and egress costs all become part of the storage decision.
Why separate compute from storage?
Compute can be provisioned or replaced relatively easily, while moving hundreds or thousands of terabytes of data can take significant time, bandwidth, and cost. Decoupling the two allows compute and storage to scale independently and makes it possible to bring workloads to the data rather than continually moving large datasets to the compute.
What are the benefits of shared storage compared with storage tied to individual servers?
Shared storage avoids capacity becoming stranded on individual compute nodes and allows storage to be allocated where it is needed. If a compute node fails or requires maintenance, workloads can move while the underlying data remains available. Functions such as snapshots, RAID, and resiliency can also be handled by the storage platform rather than consuming compute resources.
How do you choose between object, file, and block storage?
There is no single storage type that is inherently better than the others. Object storage suits large volumes of unstructured data such as backups, archives, data lakes, and AI datasets; file storage supports shared file systems as well as analytics and AI pipelines; and block storage is suited to latency-sensitive applications, virtual machines, and other workloads requiring disk-like access. The right choice depends on the workload.
AI workloads can consume enormous datasets repeatedly across ingestion, preparation, training, inference, and archival. Expensive GPU resources can sit idle if storage or the network cannot deliver data quickly enough, so latency, sustained throughput, and the ability to handle many parallel requests become important parts of AI infrastructure design.
How does private connectivity change backup and recovery?
Backup performance and recovery times can be constrained by available internet bandwidth. A dedicated private path to storage can provide additional capacity without competing with production internet traffic, helping organizations continuously move backup data and restore large datasets more quickly when recovery is required.
What does cyber recovery require beyond simply having a backup?
Organizations need to know that recovery data is trustworthy, isolated, and usable after an incident. Immutable storage, clean recovery points, segmentation, and controlled connectivity can all form part of the recovery architecture, shifting the question from simply “Do we have a copy?” to “Can we safely restart the business from it?”
Does multicloud mean keeping a separate copy of data in every cloud?
Not necessarily. Maintaining copies across multiple clouds can introduce additional cost, governance, security, egress, and lifecycle challenges. An alternative is to separate compute decisions from data-location decisions and use high-performance connectivity so multiple environments can access a common dataset where appropriate.
What should organizations ultimately design their storage architecture for?
Optionality. Rather than trying to predict exactly where workloads will run years from now, organizations can design infrastructure so compute, network, and storage can scale independently. That creates more freedom to move workloads, change cloud providers, recover in another environment, or adopt new infrastructure without unnecessarily moving the underlying data.
