Illustrative sample, not a client engagement. No results are published because this scenario has no verified outcome.
Challenge
Nightly file drops and one-off scripts feed a reporting database. Failures are noticed when a dashboard is empty, and nobody can say which transformation last changed a measure.
Solution overview
Scheduled pipelines replace the scripts. Each stage has a contract, a row-count check, and a record of the source file or extract it processed. Analysts keep the same warehouse tables while engineering gains a path they can rerun.
Approach
- Map the current scripts, schedules, and the reports that depend on them.
- Separate ingestion, conformance, and presentation models.
- Add data quality checks before published tables are refreshed.
- Leave rerun and alerting steps the operations team can use.
Technologies
- Python
- SQL
- PostgreSQL
- Azure Data Factory