RetailPulse DataOps is a production-style retail data engineering and observability platform that processes multi-source retail data through Bronze, Silver, and Gold layers while automatically validating data quality, detecting anomalies, monitoring pipeline health, and generating incident intelligence.
Raw Retail Data ↓ Bronze Ingestion ↓ Data Quality Engine ↓ Quarantine Invalid Records ↓ Silver Transformation ↓ Gold Analytics ↓ Revenue Anomaly Detection ↓ Monitoring & Health Reporting ↓ Incident Intelligence
- Bronze, Silver, and Gold data architecture
- Multi-source retail data ingestion
- Automated data-quality validation
- Invalid-record quarantine
- Business analytics generation
- Revenue anomaly detection
- Pipeline monitoring and health reporting
- Automated incident intelligence and recommendations
- Pipeline execution logging
- Pytest-based automated testing
Python | Pandas | PyArrow | Parquet | SQL | Pytest | Git | GitHub
data/raw/ → Sample source datasets src/ → Data pipeline and intelligence modules tests/ → Automated tests sql/ → SQL analytics docs/ → Project documentation
Install dependencies:
pip install -r requirements.txt
Run the pipeline:
python -m src.pipeline
Run tests:
python -m pytest -v
PIPELINE COMPLETED SUCCESSFULLY
The pipeline validates incoming retail data, processes valid records through analytical layers, identifies anomalies, and generates recommended actions for detected data incidents.
Manasa Varala