january 30, 2026 · 1 min read · distributed-systems, data-migration, hubspot
Outages are loud. Data corruption is quiet.
Moving 10M+ CRM records across distributed systems, and why the failure mode worth designing for isn't downtime.
A server didn't crash. The database didn't go down. But thousands of records could have been wrong.
That's the failure mode you design for when moving 10M+ records across distributed systems. I've been architecting large-scale CRM integration pipelines where the real risk wasn't downtime. It was silent data corruption.
The system processes 10K+ records an hour through parallel microservices: queue-driven jobs, idempotent processing, retry-safe flows and strict relationship integrity checks. It's built mostly in Node.js and Python, with observability and validation layers baked in, so every record is traceable during large migrations.
The lesson: in distributed systems, outages are loud. Data inconsistency is quiet, and far more dangerous.
This is why strong data infrastructure matters just as much as AI or features. I'm excited about building more at the intersection of distributed systems, large-scale data pipelines and AI infrastructure.
Originally posted on LinkedIn.