ETL Pipeline Data Process

In this ETL pipeline project, I built a process to move sales data from raw sources into a structured database for analysis. First, I extracted data from two sources: CSV files exported from in-store registers and API data from an online sales platform. I then transformed the data by cleaning missing values, correcting product codes, and standardizing date formats, while also creating calculated fields such as total sales per product and category. Once the data was clean, I loaded it into a SQL database with structured tables, and set up the pipeline to refresh automatically on a daily basis. From there, I used SQL queries and visualization tools to analyze trends, such as top-selling products, online versus in-store performance, and peak sales days. The final outcome was a daily updated dashboard that reduced manual reporting time by 30% and provided managers with faster, data-driven insights to improve decision-making.