A lake-then-warehouse architecture where raw operational data lands immutably in S3, is transformed by event-driven Python jobs, passes data-quality gates, and is modeled in Redshift for analytics and AI consumption.
Operational data was fragmented across systems with inconsistent schemas and no shared definition of truth, so every analysis started with reconciliation instead of insight.
- —A single centralized analytical platform instead of fragmented extracts
- —Faster access to reliable, modeled data
- —Pipeline failures observable and recoverable
Immutable raw zone
Raw payloads land untouched in S3, making every downstream transformation reproducible and replayable.
Event-driven over cron
Lambda triggers on arrival keep latency low and cost proportional to volume rather than schedule.
Quality gates before load
Row counts, null profiles, referential and uniqueness checks run before the warehouse load, so bad data fails loudly and early.
Infrastructure as code
Terraform-managed resources make environments reproducible and changes reviewable.
Late-arriving and duplicate records
Deduplication keys and merge logic keep the warehouse correct under out-of-order delivery.
Schema drift
Normalization at the transformation layer absorbs upstream changes without breaking analytical models.
Operability
CloudWatch metrics and alerting expose pipeline failures, latency and volume anomalies in production.
- →Replayability is the cheapest insurance a pipeline can buy
- →Data quality checks belong in the pipeline, not in the dashboard
- →Modeling decisions determine how usable a warehouse feels