Silver Layer: Data Transformation & Quality

Published: November 2025 | 35 min read | Code on GitHub

Transforming Raw Data into Analysis-Ready Format

In this installment, we'll implement the Silver Layer transformations that clean, validate, and prepare our bronze data for analysis and machine learning.

Key Components

1. Text Cleaning Pipeline
2. HTML/Unicode normalization
3. Medical abbreviation expansion

4. Clinical note section identification

5. PII Redaction

6. Named entity recognition for PHI
7. Secure redaction with audit trails

8. Pattern-based detection for medical IDs

9. Business Logic

10. Specialty classification
11. Symptom extraction

12. Temporal feature engineering

13. Data Quality

14. Great Expectations validations
15. Automated anomaly detection
16. Data quality dashboards

Implementation Highlights

# Example: Text cleaning pipeline
def clean_clinical_text(text: str) -> str:
    """Clean and normalize clinical note text."""
    # Remove HTML tags
    text = re.sub(r'<[^>]+>', ' ', text)

    # Expand common medical abbreviations
    text = MEDICAL_ABBREVIATIONS.sub(
        lambda m: MEDICAL_ABBREVIATION_MAP[m.group(0).lower()], 
        text, 
        flags=re.IGNORECASE
    )

    # Standardize whitespace and normalize unicode
    text = ' '.join(text.split())
    return unicodedata.normalize('NFKC', text)

Performance Optimization

• Partitioning Strategy:

  # Partition by date and specialty for efficient querying
  (df.write
     .partitionBy("ingest_date", "medical_specialty")
     .parquet("s3://silver-layer/clinical_notes/"))
  

• Z-Ordering:

  -- Optimize for common query patterns
  OPTIMIZE silver.clinical_notes
  ZORDER BY (patient_id, note_date);
  

Monitoring & Alerting

• Automated data quality checks
• Drift detection for text statistics
• Alerting on PII detection

← Part 3: Bronze Ingestion | Continue to Part 5: Gold Layer & Feature Store →