Data pipelines / Scraping
More than 2 million vehicle records scraped and parsed into PostgreSQL in a couple of days
Large-scale vehicle data
Five parallel Python scraping workers pull vehicle listings at scale. Each worker's output funnels into a shared parsing and validation step, which normalizes records into a consistent structure before writing to PostgreSQL. More than 2 million records moved through this pipeline in a couple of days.
The operational problem
A large dataset needed to be collected, parsed into a consistent structure, and stored reliably.
How the system handles it
A Python pipeline collected data at scale, parsed records, and stored the results in PostgreSQL.
ENGINEERING DECISIONS
Logic & boundary design
Unstructured data processing, context extraction, and reasoning.
Deterministic conditions, status routing, and strict validation.
Final approval triggers, manual overrides, and audit checkpoints.
API fallbacks, database connectors, and custom Python integrations.
SYSTEM OUTCOME
More than 2 million records were scraped and parsed into PostgreSQL in a couple of days.
“Ahsan was able to quickly scrape and parse 2+mil records from a website in a couple days”
Upwork client
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