Engineering a Robust ETL Pipeline for Data-Driven Insights
Data Engineering
In this project, I harness the power of Postgres and Python to construct a sophisticated ETL (Extract, Transform, Load) pipeline. The core of this endeavor involves meticulous data modeling, where we craft and implement fact and dimension tables within a star schema tailored for targeted analytics. This pipeline proficiently processes and transfers data from diverse files located in two distinct local directories, channeling them seamlessly into our structured Postgres tables. Integrating Python and SQL ensures a fluid efficient data flow, laying the groundwork for insightful, data-driven decision-making.