By Brian Brown, Senior VP & General Manager of Public Sector
Many of our recent conversations have been about a gap: attacker dwell time versus retention windows, threat intelligence arriving late, baselines that never see a full year. Those are real, and they are what got us building this platform in the first place. But somewhere in the last year of deployments, the conversation started shifting from “can you close this gap” to “wait, what else can we do with this.” That second question is what this post is about.
Why the question changes at petabyte scale
For most organizations this has come down to a forced choice between two bad options. Retain everything, and watch cost and performance move in the wrong direction together as the dataset grows: more hardware, slower queries, a budget conversation that eventually crowds out the analysis conversation. Or filter, sample, and pre-aggregate aggressively enough to keep cost and performance under control, and accept that you’ve built holes into your own data before you ever knew which record would matter. Most of the industry has been managing that trade-off rather than solving it: tier the data, sample the stream, filter before it lands, and treat “deep history” and “fast query” as things you must choose between.
The OcientAIQ™ Unified Data Platform was built on the premise that this is a solvable engineering problem, not a permanent law of analytics. The difference is architectural: ingest rate, storage depth, and query concurrency don’t share one resource pool that you ration between them. They scale independently and linearly (add nodes for ETL throughput, add storage for retained history, add SQL compute nodes for concurrent analytic load) in whatever mix an environment actually needs, all the way out to petabyte and exabyte scale.
What we hear consistently from customers who deploy at that scale is that the total cost of ownership often comes in lower than what they were already paying for a smaller, gappier version of the same visibility, which is a hard thing to fathom until you’ve seen it running. More than one experienced practitioner has told us the moment it really landed for them was watching one of our systems perform, at production scale, exactly as advertised, and then spending the next hour exploring what else it could do.
Customers also tell us that when they deploy OcientAIQ at real scale, the traditional trade-off stops applying when the volumes they’re running reach hundreds of millions of events an hour, trillions of rows retained, or years of full-resolution history. Once that happens, people don’t just do the same analysis faster. They start asking questions they’d previously filed under “not possible with our data” and never revisited. That reframing is the point of this post: not a faster version of what you already do, but a different list of things you’d consider doing at all.
Three examples of the shift
These are composites drawn from conversations with customers across different industries; details are generalized and anonymized per our standard practice.
- Tracking and predicting collision risk in orbit. Space situational awareness requires tracking a growing population of objects (active satellites, spent stages, debris) and computing conjunction risk between them over time. The hard part isn’t the physics; it’s that the volume of tracking data and the number of object pairs to evaluate both grow faster than most analytics platforms can keep up with, which historically has forced operators to prioritize which objects get watched closely and accept blind spots elsewhere. A platform that can hold years of tracking history at full resolution and run the pairwise proximity math across it interactively changes what “watched closely” means: it can mean everything, not a prioritized subset.
- Multi-year anomaly detection across operational logs. Several customers describe the same shape of problem in different domains: an anomaly that only becomes visible against years of history, not weeks. A slow drift in equipment behavior, a rare failure mode that only recurs on a multi-year cycle, a pattern that looks like noise at 90 days of context and looks like a clear signal at two years. The same shape shows up in security telemetry too, and it turns out not to be a security-specific argument at all: it’s a statement about what long, full-resolution history makes visible that short history structurally cannot.
- Rapid market and buyer identification in AdTech. One customer’s use case centers on matching potential ad inventory or audiences to buyers in something close to real time, working against a dataset that grows continuously and needs to stay fully queryable rather than archived. The identification logic itself isn’t exotic (it’s pattern matching and scoring against a reference set), but doing it at the volume and latency the business needs, without falling back to a sampled or stale version of the data, is the same full-resolution-plus-interactive-query problem we keep returning to across our writing on this platform, just in a commercial context instead of a security one.
What’s common across all three
Pull the specifics away and the same three requirements show up every time:
- Full-resolution capture, because sampled or aggregated data quietly deletes exactly the rare event you’re trying to find.
- Retention deep enough to cover the phenomenon’s actual cycle, whether that’s an orbital period, an equipment failure mode, or a market trend, rather than whatever window the storage budget allowed.
- Interactive query against all of it, because the ability to iterate in real time unlocks a different kind of analysis than an overnight batch job.
The first two requirements – full-resolution ingest and long retention – are architectural problems in their own right. The third, interactive querying at scale, is where TimeKey® indexing does the work. OcientAIQ builds multiple indexes across a table’s columns at load time rather than committing to a single clustering key , which is what lets a query pick whichever access path the question actually needs instead of scanning rows because the one index built doesn’t match the one column this particular query cares about. At small scale that distinction barely matters. At the volumes described above, it’s the difference between a query that returns while you’re still in the room and one that returns after the meeting ends.
None of this works, either, if the data can’t get onto the platform in the first place. Every one of these use cases is generating data continuously and at volume (tracking feeds, machine telemetry, ad impressions), and an analytics layer that can index brilliantly but can’t ingest at the rate the source produces just moves the bottleneck to the front door. Sustaining ingest at hundreds of millions of events an hour is its own challenge. Fast queries only matter if the data can land fast enough to be queried in the first place.
The economics that make this sustainable
There’s a reason none of the three examples above describe running these questions occasionally instead of continuously. Under a pricing model where cost scales with how much you query or how much data a search scans, the incentive is to ask the expensive question rarely, which is exactly backward from what these use cases need. A conjunction-risk calculation you run once a week because each run is expensive is a materially worse safety posture than one you can run continuously. An anomaly search you ration because of per-scan cost will miss the anomaly that shows up between runs.
OcientAIQ solutions are priced around the fixed capacity of the deployment rather than metering each query, which means the economics point the other way: once the hardware is in place, running the analysis again, or running it continuously, costs the electricity and compute it takes, not a new invoice line. That property matters more, not less, as more of this workload gets handed to AI agents that run continuously rather than analysts who run a query and go do something else. An agent making the same conjunction-risk check every few minutes, all day, is a very different cost proposition under fixed capacity than it is under consumption pricing, and it’s one more reason we think the right architecture pushes the actual analytics down into the query engine rather than asking an agent to reason through the data itself.
Fixed-capacity pricing also only means what it means if the customer actually owns the capacity. OcientAIQ deploys as an open, hardware-agnostic architecture inside the organization’s own data centers, private cloud, or air-gapped environment, not as a service where the data has to leave the building to be useful. That distinction matters at exactly the scale this post is about: an organization running petabytes of tracking data, operational logs, or continuously generated commercial data is, by definition, running some of its most sensitive and valuable data through the platform. Owning the hardware it runs on, and controlling where that hardware physically sits, is not a compliance checkbox at that point. It is the difference between a platform you operate and a platform that operates on your data somewhere else.
Depending on the organization, that ownership question gets described differently. For a multinational operator, it’s a data sovereignty question: whose legal jurisdiction governs the data given where it physically sits. For a domestic operator working with regulators who care less about jurisdiction and more about vendor concentration and auditability, it’s closer to a third-party-risk and operational-resilience question. The label changes; the architectural answer doesn’t.
Where this goes next
We don’t think we’ve seen the full list of what becomes possible at this scale, and we’d be skeptical of anyone who claimed they had. What we can say from the deployments we’ve done is that the pattern repeats: give a team full-resolution data, years of it, indexed for interactive query, at a cost structure that doesn’t punish curiosity, and the first thing they build is rarely the last thing they ask for.
Contact us to learn more.