Designing Automated ETL & Arabic NLP Pipelines for Government Analytics

In nationwide educational reporting, collecting quarterly evaluation metrics across 100+ disparate schools frequently degrades into an operational nightmare of inconsistent Excel sheets, mismatched column headers, and unnormalized Arabic survey responses.

To eliminate manual consolidation, we designed a pipeline based on three core layers:

1. **Deterministic Validation Ingestion**: Every file upload is hashed and validated against strict Pydantic schemas. Corrupt rows are isolated into audit logs rather than crashing the batch.
2. **Arabic Text Normalization**: Automated removal of diacritics, unified character mappings (e.g. normalizing variations of Alef and Yaa), and custom stopword pruning.
3. **Database-Level Aggregation**: Instead of executing calculations in Python application memory, analytical queries are compiled into optimized PostgreSQL views with pre-computed summary rollups.

**Measurable Results:**
- Report compilation time fell from 4 weeks of manual labor to under 3 minutes.
- Zero data corruption across 15,000+ individual teacher and student survey records.

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