The solution's performance and density are excellent.
Typically, there is a trade-off. You can have incredibly dense storage in a small footprint sometimes, but the trade-off to that is you need a lot of horsepower to access it, which ends up counterbalancing the small footprint. Then, sometimes you can have very fast access to a storage array, but that usually requires a more comprehensive infrastructure.
This kind of balance, to somehow fit it all into one chassis, in a 4U server rack, is unheard of. You have the processing proxy accessing the data and almost a petabyte of flash accessible.
It's a very small footprint, which is important to our type of industry because we don't have massive servers.
We have benefited from this technology because we were able to centralize a lot of workflows. There is normally a trade-off, where you can have very fast local storage on the computer, but in a collaborative environment that's counterproductive because it requires people to share files and then copy them onto their system in order to get the very fast local performance. But with Pavilion, basically, you get that local NVMe performance but over a fabric, which makes it easier to keep things in sync.
We have been able to consolidate storage and as part of a multi-layer storage system, it plays a very important part. For us, it cuts down on costs because we essentially get an NVMe tier that's large enough to hold everyone's data, but the other thing for us is time and collaboration. Flexibility is worth a lot to us, as is creativity, so having the resources to do that is incredibly valuable.
If we wanted to do so, Pavilion could help us create a separation between storage and compute resources. It's one of those things where, in some environments, such as separation is natural and in other environments, there's an inclination to minimize the separation between compute and data. But to that point, Pavilion has the flexibility to allow you to really do whatever you want.
In that sense, you have some workloads where compute is very close to the data, such as iterative stuff, whereas we have some things where we simply want bulk data processing. You can do any of that but for us, that type of separation is not necessarily something we are concerned with, just given our type of workflows. That said, we have that flexibility if necessary.
This system has allowed us to ingest a lot of data in parallel at once, and that has been very useful because it's a parallel system. It's really helped eliminate a lot of the traditional bottlenecks we've had.
Pavilion could allow for running additional virtual machines on existing infrastructure, although in our case, the limitation is the core densities in our hardware. That said, it is definitely useful for handling the storage layer in a lot of our VMs. The problem is that the constraints of our VM deployments are really in just how many other boxes we have to handle the cores and the memory.