Data pipeline
Migrate CSV data to a live pipeline
Replace brittle, hand-cleaned spreadsheets with a resilient pipeline that validates and syncs data automatically.
The problem
Onboarding new data means a person exporting spreadsheets, cleaning them by hand, and importing them one file at a time. It works — until volume grows. Every addition is hours of manual prep, and every manual step is a chance for a quiet error to reach the data you rely on.
- Hours of manual spreadsheet prep for every new source.
- Silent errors slipping through hand-cleaning into downstream data.
- No visibility into which records failed, or why.
The solution
We replace the CSV shuffle with a pipeline that pulls, validates and transforms data on a schedule, writing clean records straight into the destination system. Crucially, it fails loudly — bad rows are quarantined and reported, never silently dropped.
The transformations live in code, not in someone's spreadsheet. That makes the data trustworthy and the process repeatable — a non-event to add the next source, and clean enough for ML benchmarking.
How it works
Map & contract the data
We document every field, its type and its rule, turning tribal spreadsheet knowledge into an explicit validation contract.
Build the pipeline
Scheduled ingestion with retries and idempotency; rows that violate the contract are quarantined and reported, not dropped.
Go live with a health view
We roll out behind a dashboard that shows every run at a glance, and hand it over fully documented — you own it.