How sriram-118/retail-sales-data-pipeline-business-analytics-dashboard works
This repository presents a retail-sales analytics workflow: CSV sales and dimension data are ingested, cleaned, transformed into a relational star schema, and used for an interactive Power BI dashboard. The tree contains the four CSVs and the PBIX, but no Python or database source, so those documented runtime stages have no supported source paths; their internal wiring is represented only at the documented workflow level.
Main entry points: Final Output.pbix Power BI Dashboard · fact_sales.csv Sales Facts · dim_customer.csv Customer Dimension · dim_date.csv Date Dimension
Components 10
Data Inputs
- Sales Facts
fact_sales.csv - Customer Dimension
dim_customer.csv - Date Dimension
dim_date.csv - Product Dimension
dim_product.csv
Data Pipeline
- Python Ingestion
- Data Cleaning
- Data Transformation
- Star-Schema Database
Analytics
- Power BI Dashboard
Final Output.pbix
Other components
- Analyst
Connections 9
- Sales Facts → Python Ingestion: ingested by
- Customer Dimension → Python Ingestion: ingested by
- Date Dimension → Python Ingestion: ingested by
- Product Dimension → Python Ingestion: ingested by
- Python Ingestion → Data Cleaning: passes data
- Data Cleaning → Data Transformation: passes data
- Data Transformation → Star-Schema Database: loads data
- Star-Schema Database → Power BI Dashboard: provides analytics data
- Analyst → Power BI Dashboard: analyzes results