You Captured It. Now What? Managing Mission Data at Petabyte Scale

With data increasingly driving the DoD mission, teams tend to focus their energies on solving the ingest problem. That makes sense: A single USAF unmanned aerial vehicle can generate 70 terabytes of data in 14 hours, Deloitte reports, and capturing such vast volumes of data without loss is genuinely difficult.

But once you’ve solved that, a second problem is already growing in the background you’re quickly generating petabytes of data.

For program managers, systems architects, network security engineers, and Intelligence, Surveillance, & Reconnaissance (ISR) mission planners, the RF/IQ streams, full-packet network captures, and ISR feeds aren’t slowing down. They’re compounding, and the gap between data growth and the infrastructure built to manage it is widening with every operational cycle.

Capturing data at speed is step one. Managing the cumulative dataset — tiering it intelligently, indexing it for instant recall, and replaying it on demand — is also a significant challenge. It’s an architectural challenge, and it’s the one most organizations don’t recognize until they’re already drowning.

The data growth reality for RF and network missions

With so many mission sets dependent on data, it’s important for military planners to understand the scale and impact of these challenges.

Modern ISR missions and Defensive Cyber Operations generate data continuously and simultaneously across multiple sensors and collection points. The RF data and full-packet network traffic doesn’t come in brief bursts: There’s sustained throughput, mission after mission.

Today, the volume of data being generated is growing exponentially. But storage infrastructure and analytical throughput are growing linearly, which means it is too slow to keep pace with the need. The gap is structural, and it’s not temporary.

This has practical consequences for the mission. Teams that don’t have a data lifecycle plan end up making bad choices under pressure — deleting data they’ll later wish they had, or keeping everything on fast (expensive!) storage until costs become untenable.

Both of these problem domains, RF/signals and network packets, face the same underlying structural challenge. Organizations increasingly need to manage both simultaneously.

The three-tier storage model: Hot, Warm, and Cold

Planners and analysts need data at a moment’s notice. They need instant access to fresh data while it’s relevant, along with the ability to quickly and easily access less-urgent data stores. A tiered-storage approach can help meet the operational demand here.

• Hot tier: NVMe (Non-Volatile Memory Express) delivers immediate access with the lowest latency. It supports the data you’re most likely to need right now: Recent captures, active investigations, ongoing mission windows. Axellio’s SensorXpress and PacketXpress both write to NVMe first, ensuring lossless capture at full ingest speed. The limitation here is that the hot tier is finite and can be expensive. It’s not the place where you keep everything forever.
• Warm tier: At this level, archival or Hard Disk Drive is used to manage data that’s past its immediate operational window but still operationally relevant: Recent enough that it might be recalled for forensic analysis, mission debrief, or pattern-of-life work. Automated tiering moves data from hot to warm based on age or policy, freeing NVMe without manual intervention. This tier is still local, still fast enough for near-real-time recall.
• Cold tier: Cloud or off-site HDD storage comes into play for long-term retention: For compliance, historical analysis, or future AI/ML training datasets. There’s a significant cost savings here compared to keeping everything on-premise at high-performance tiers. The key architectural requirement is that cold-tier data must remain searchable, retrievable, and replay-able — not just archived in a format that requires a manual extraction process to use. Axellio’s architecture natively supports many cold tier implementations without compromising data integrity or requiring re-ingestion.

The value of a tiered model isn’t just cost: It’s operational discipline. As mission needs unfold, your team always knows where the data is, how to get it, and what it will cost to retrieve it. That’s not a storage conversation. It’s a mission-readiness conversation.

Store and recall: Data ready at a moment’s notice

The point of tiered storage isn’t just to organize data. The point is to enable mission success. To that end, a tiered approach ensures that any data, from any point in the operational timeline, can be recalled and usable within seconds to minutes, not hours. This architectural vision ultimately enables key operational capabilities.

Both PacketXpress and SensorXpress are built around time-indexed storage. Every packet, every I/Q sample is timestamped and can be quickly queried. There’s no hunting through raw files: You query by time window, by sensor, by frequency range, or by network flow.

This has direct mission outcomes. It enables forensic investigations after a security incident; mission debrief and after-action review; algorithmic validation (running new detection logic against historical data); and training of AI/ML models on real operational data rather than synthetic datasets.

Axellio’s architecture handles simultaneous read/write as a core design requirement, not an afterthought. The result is thatany analyst can reach any data, from any point in time, at the speed of mission without being starved for data from the live capture.

Replay: The capability most teams don’t have until they need it

Replay is the ability to push stored data back through the analysis pipeline, at original timestamps, at variable rates, or targeted to specific tools — as if the mission were happening live again.

For RF, SensorXpress supports replay actions such as reprocessing signals through updated algorithms, testing new detection signatures against real spectrum data, and validating EW responses against historical emitter behavior. And for packet network traffic, PacketXpress enables actions such as teams to re-run a capture through a new Intrusion Detection System ruleset, validating that a threat would have been caught with updated signatures in support of legal or compliance review.

Replay can’t be a bolt-on: It has to be native to the storage and distribution architecture. That’s why Axellio builds it into both SensorXpress and PacketXpress, rather than requiring a separate workflow. And replay at 200 Gbps or higher means you can run a compressed timeline, reviewing weeks of data in just hours.

Making hyperscale sustainable: The architectural principles

What does it take to make this work at real scale?

• Unified platform — Managing two separate data silos with different tools, retention policies, and query interfaces doubles operational overhead. As a unified platform across RF and network data, the Xpress Platform handles both.
• Policy-driven automation — Manual data management doesn’t scale to petabyte volumes. With the right solution in place, tiering, retention windows, and deletion schedules are defined by policy and enforced automatically.
• Hardware-agnostic deployment — With the same software running in a forward-deployed edge rack or a rear-area data center, architectural decisions made at the platform level carry across deployment scenarios.
• Cost discipline — The combination of Hot/Warm/Cold tiering with a common search/query interface means organizations can dramatically reduce storage costs without sacrificing access to historical data.

The question isn’t whether you can capture mission data at speed. The question is whether you can find it, replay it, and act on it six months from now. Axellio’s Xpress Platform is purpose-built for exactly this challenge -delivering secure, real-time data intelligence at petabyte scale. Explore PacketXpress and SensorXpress, and learn how Axellio can transform your organization’s data operations at axellio.com.