In 2021, I was developing software for an aerospace manufacturer and met with our machine learning team to discuss innovative approaches for tracking FOD (free-orbiting debris), a major security and operational concern in the industry. What struck me wasn’t the algorithms or tracking equipment, but the terabytes of data (up to petabytes) that were being produced.
Old-school problems of limited hardware resources and inefficient data compression were bottlenecking cutting-edge visual learning models and traditional tracking solutions alike. The team was smart and could fine-tune quickly, but the real challenge was making sure our infrastructure could scale with them.
In aerospace, performance hinges on how fast systems can absorb and interpret massive telemetry streams, and storage is often the silent limiter. When you’re generating terabytes to petabytes of data in a single test cycle, even a brief stall in the storage layer becomes a bottleneck. A few milliseconds of delay between what’s happening and what the system can write, index, or retrieve doesn’t just slow things down. It can compound through an entire run.
