Python • Flask • PostgreSQL • React
Full-Stack Business Data Pipeline
A two-repository full-stack project that combines automated ETL processing with a creator analytics
dashboard. Raw business data is cleaned and loaded into a PostgreSQL warehouse, then surfaced through
authenticated product views for real-time performance tracking and growth insights.
This project reflects the combination of two related repositories into one end-to-end business
analytics system. The backend ETL layer handles data extraction, normalization, and warehouse
loading, while the dashboard layer turns that curated data into a product experience for creators
who need timely, actionable performance insights.
2
Combined Repos
90%
Manual Cleaning Reduced
JWT
Secure Access
SQL
Analytics Queries
- Architected an automated ETL pipeline that processed raw business datasets into a PostgreSQL data warehouse.
- Reduced manual data cleaning time by an estimated 90% through Python-based normalization scripts.
- Developed an interactive React dashboard with JWT authentication for secure creator access.
- Provided real-time performance tracking and actionable growth insights via optimized SQL analytical queries.
ETL Repository
- Extracts raw business data and prepares it for downstream analytics.
- Uses Python and Pandas to normalize inconsistent records and standardize fields.
- Loads curated data into PostgreSQL so reporting and dashboard queries run against a structured source of truth.
- Keeps extract, transform, and load responsibilities separated for maintainability.
Dashboard Repository
- Builds the product-facing analytics experience for creators.
- Uses React for the frontend and Flask-backed services for data access.
- Secures access with JWT authentication.
- Surfaces performance metrics and growth insights using optimized SQL-driven views.
Performance Tracking
Shows creators real-time views of business and content performance.
JWT Authentication
Protects dashboard access through authenticated sessions.
Warehouse-Backed Queries
Reads from curated warehouse data instead of raw source files.
Growth Insights
Transforms processed data into decisions creators can act on quickly.
- Extract: ingest raw business datasets from source files and upstream inputs.
- Transform: normalize records with Python scripts and Pandas operations.
- Load: move cleaned data into PostgreSQL for consistent analytics access.
- Serve: expose dashboard-ready data through Flask-backed application logic.
- Visualize: present metrics and trends in an interactive React interface.
Python
ETL Logic
Flask
Backend
PostgreSQL
Warehouse
React
Frontend
- Backend: Python, Flask
- Data Engineering: ETL, Pandas
- Database: PostgreSQL, SQL
- Frontend: React
- Authentication: JWT
- Version Control: GitHub