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.
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.
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.
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.
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 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.
What does it take to make this work at real scale?
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.
Petabyte-scale data management is the process of storing, organizing, indexing, protecting, and retrieving extremely large datasets that measure in petabytes (1,000 terabytes or more). In defense, cybersecurity, and intelligence operations, it ensures that mission-critical data remains accessible for analysis, investigations, and future operational needs.
Mission systems generate far more data than ever before. RF sensors, network packet captures, ISR platforms, and cyber monitoring tools continuously produce massive data streams. While capturing this information is essential, storing, organizing, and quickly retrieving it becomes increasingly challenging as data accumulates over months and years.
A tiered storage strategy organizes data based on how frequently it is accessed. Frequently used data remains on high-performance storage, recently collected data moves to lower-cost storage, and archival data is retained on long-term storage while remaining searchable. This approach balances performance, scalability, and storage costs.
Hot storage contains active mission data that requires immediate, low-latency access. Warm storage holds recently collected information that may still be needed for investigations or analysis. Cold storage preserves historical datasets for long-term retention, compliance, AI training, and future investigations while keeping costs under control.
Historical mission data often becomes valuable long after it was first collected. Analysts may need to investigate past cyber incidents, validate new detection algorithms, conduct forensic investigations, or identify long-term behavioral patterns. Fast search capabilities allow organizations to access historical information without lengthy manual retrieval processes.
Data replay is the ability to stream previously captured data back through analysis tools as though it were occurring live. This allows organizations to test new analytics, validate detection signatures, investigate historical incidents, train personnel, and improve AI and machine learning models using real operational data.
Replay enables analysts to revisit historical network traffic or RF data using newer tools and detection methods. Instead of relying on synthetic datasets, teams can evaluate real mission data, improve threat detection, validate electronic warfare responses, and perform detailed after-action reviews without recreating the original event.
Time-indexed storage allows analysts to quickly locate data from a specific timeframe without manually searching through massive files. Investigators can query data by timestamp, sensor, frequency, or network flow, significantly reducing the time required to support forensic analysis and operational decision-making.
Many traditional storage systems force organizations to choose between ingesting new data and analyzing existing data. Architectures that support simultaneous reading and writing allow continuous data collection while analysts access historical information, preventing operational bottlenecks during active missions.
Manual storage management becomes impractical when organizations manage petabytes of information. Policy-driven automation automatically moves data between storage tiers, applies retention policies, and manages lifecycle rules, reducing administrative effort while ensuring important data remains available when needed.
Managing RF data, packet captures, and other mission data within separate systems increases operational complexity. A unified platform provides consistent search, storage, retention, and replay capabilities across multiple data types, simplifying operations while improving efficiency and reducing infrastructure overhead.
Organizations should build scalable data architectures that support lossless capture, intelligent tiered storage, rapid search, automated lifecycle management, and native replay capabilities. Planning for the full data lifecycle, not just ingestion, helps ensure mission data remains useful months or years after it is collected.