Amazon TicketDB Data Pipeline
Designed and built an end-to-end ETL pipeline for processing and analyzing Amazon ticketing data.
Raw transactional data from ticketing systems is unstructured and difficult to query for insights. Without a proper pipeline, analysts waste time on manual data wrangling instead of actual analysis.
Designed an ETL pipeline that ingests raw Amazon TicketDB data, applies cleaning and transformation rules, and loads the result into a structured relational format — enabling clean, repeatable analysis.
Produced a well-structured, query-ready dataset with a repeatable pipeline that demonstrates core data engineering skills: data modeling, transformation logic, and pipeline architecture.
A data engineering project focused on constructing a robust pipeline that extracts raw ticketing data, applies structured transformations, and loads it into a queryable database for downstream analysis. Built as part of an applied data science curriculum with real-world pipeline architecture principles.