Medallion Architecture Explained: Building Reliable Data Lakehouses

What is Medallion Architecture?

Medallion architecture is a data design pattern that organizes your data lakehouse into three progressive layers:

• Bronze Layer: Raw, unprocessed data
• Silver Layer: Cleaned, validated, and transformed data
• Gold Layer: Business-ready aggregated data

Think of it like refining crude oil—each stage adds value and removes impurities.

Why Use Medallion Architecture?

1
Bronze (Raw) → Silver (Clean) → Gold (Aggregated)
2
   70% quality    95% quality      99% quality

Implementing the Bronze Layer

Purpose: Capture raw data exactly as it arrives. Never modify source data.

# Bronze layer ingestion example
from airflow import DAG
from airflow.operators.python import PythonOperator
 
def ingest_to_bronze(**context):
    """
    Ingest raw data with minimal processing
    """
    source_data = read_from_source()
    
    # Add metadata only
    enriched_data = {
        'data': source_data,
        'ingestion_timestamp': datetime.now(),
        'source_system': 'external_api',
        'raw_schema_version': 'v1'
    }
    
    # Write as-is to bronze
    write_to_bronze(enriched_data)

Key Principles:

• Preserve original data format
• Add audit columns (timestamp, source, version)
• Never filter or transform
• Keep everything, even bad data

Implementing the Silver Layer

Purpose: Clean, validate, and standardize data for downstream use.

def bronze_to_silver(**context):
    """
    Transform bronze to silver with validation
    """
    bronze_data = read_from_bronze()
    
    # Data quality checks
    validated_data = validate_schema(bronze_data)
    cleaned_data = remove_duplicates(validated_data)
    standardized_data = apply_business_rules(cleaned_data)
    
    # Add quality metrics
    quality_score = calculate_quality_score(standardized_data)
    
    if quality_score < 0.95:
        raise DataQualityException(f"Quality score too low: {quality_score}")
    
    write_to_silver(standardized_data, quality_score)

Key Principles:

• Schema validation
• Deduplication
• Standardization (dates, formats, naming)
• Data quality testing
• Type casting and normalization

Implementing the Gold Layer

Purpose: Create analysis-ready aggregations and business metrics.

def silver_to_gold(**context):
    """
    Aggregate silver data for analytics
    """
    silver_data = read_from_silver()
    
    # Business aggregations
    daily_metrics = aggregate_by_date(silver_data)
    kpi_metrics = calculate_kpis(silver_data)
    
    # Optimize for query performance
    optimized_metrics = {
        'daily_aggregates': daily_metrics,
        'kpi_summary': kpi_metrics,
        'computed_at': datetime.now()
    }
    
    write_to_gold(optimized_metrics)

Key Principles:

• Pre-computed aggregations
• Business logic applied
• Optimized for fast queries
• Denormalized for analytics

Real-World Example: Processing Medical Imaging Data

In production, we processed NIfTI brain imaging files:

1
/bronze/nifti_scans/
2
├── scan_001_raw.nii.gz  (original file)
3
├── scan_002_raw.nii.gz
4
└── _metadata/
5
    ├── ingestion_log.json
6
    └── source_checksums.json

-- Validated and standardized
CREATE TABLE silver.brain_scans (
    scan_id UUID PRIMARY KEY,
    patient_id UUID,
    scan_date DATE,
    scan_type VARCHAR(50),
    file_path VARCHAR(500),
    file_size_mb DECIMAL(10,2),
    quality_score DECIMAL(3,2),
    processed_timestamp TIMESTAMP
)

-- Analysis-ready aggregations
CREATE TABLE gold.scan_analytics (
    date DATE,
    total_scans INT,
    avg_quality_score DECIMAL(3,2),
    scans_by_type JSON,
    data_volume_gb DECIMAL(10,2)
)

Tools for Medallion Architecture

For Transformations:

• dbt: SQL-based transformations with testing
• Spark: Large-scale data processing
• Python: Custom transformation logic

For Orchestration:

• Apache Airflow: Schedule and monitor pipelines
• Dagster: Asset-based orchestration
• Prefect: Modern workflow management

For Storage:

• Delta Lake: ACID transactions on data lakes
• Apache Iceberg: Table format for analytics
• Parquet: Columnar storage format

Common Pitfalls to Avoid

Measuring Success

After implementing medallion architecture in production:

• Data Quality: 95% → 99.5%
• Processing Time: Weeks → 2 days
• Debug Time: 60% reduction
• Data Governance: Full lineage tracking

Getting Started

1. Start Simple: Begin with one data source
2. Define Quality Rules: What makes data “silver quality”?
3. Automate Testing: Use Great Expectations or similar
4. Document Transformations: Each layer’s purpose and rules
5. Monitor Metrics: Track quality scores over time

Key Takeaways

• Medallion architecture separates concerns into Bronze, Silver, Gold layers
• Each layer improves data quality incrementally
• Essential for data governance and debugging
• Works at any scale—from GBs to TBs
• Tool-agnostic: Use dbt, Spark, or custom Python

The medallion pattern isn’t just theory—it’s a proven approach for building reliable, scalable data platforms. Start with bronze, clean to silver, aggregate to gold, and watch your data quality soar.

Have questions about implementing medallion architecture? I’m happy to share more details. Connect with me on LinkedIn.