About: Python and SQL analytics engineering pipeline for data ingestion, validation, transformation, KPI generation, and analytics-ready reporting. The project connects data engineering practices with operational DevOps use cases.
The pipeline takes raw operational records through a controlled sequence of ingestion, validation, transformation, and analytical modeling. Each stage has a clear responsibility so failures can be identified, tested, and improved independently.
Raw Source / CSV
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Python Ingestion
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Data Validation & Quality Checks
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Transformation
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SQL Analytics Models
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KPI / Reporting Layer
- Python-based ingestion and transformation
- SQL schema and analytical marts
- Data-quality validation
- Testable pipeline stages
- Operational KPI generation
- Error-rate and processing-duration analysis
- GitHub Actions validation
- Clear separation of ingestion, transformation, and analytics
src/ingest.py
src/transform.py
sql/schema.sql
sql/mart.sql
data/sample_events.csv
tests/test_pipeline.py
.github/workflows/test.yml
python src/ingest.py
python src/transform.py- Daily event volume
- Successful vs failed events
- Average processing duration
- Error rate
- Top services by activity
Operational analytics can help engineering teams understand service health, failure patterns, processing performance, and workload behavior. This project therefore demonstrates the intersection of analytics engineering, automation, data quality, and DevOps observability.