Scaling Data Processing from ~1M to up to 10M Documents/Day
Growth readiness
Business Context
A growing SaaS data platform needed to process high volumes of compliance-sensitive business documents while supporting customer integrations, analytics, reporting, historical data onboarding, and future data products.
The existing system had been designed around earlier-stage assumptions and was too dependent on a central database-bound application model.
Platform Bottleneck
The core bottleneck was architectural scalability. The platform needed to move from a centralized, database-bound design toward a more decoupled cloud-native architecture capable of handling higher ingestion and processing volumes.
Actions Taken
- Redesigned the platform around looser coupling and cloud-native components.
- Used object storage and event-driven patterns to reduce pressure on the central application/database.
- Introduced pre-signed upload flows, S3 events, Lambda processing, queue-based processing, and Databricks for analytics.
- Created a platform capable of supporting integration partners, customer access, analytics, reporting, partner access, and future data products and API offerings.
Results
- Helped scale ingestion/processing capacity from approximately 1M documents/day to architecture capable of handling up to 10M documents/day.
- Created a more scalable foundation for historical onboarding and future growth.
- Reduced dependency on a central database-bound model.
- Improved platform readiness for analytics, reporting, and future data products and API offerings.
Why This Matters
If your SaaS platform is growing in volume and you are not sure whether the current architecture can support the next stage of growth, this is the kind of problem PRISM is designed to diagnose.
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