Medallion Architecture Skill

Medallion Architecture Skill

Overview

The medallion architecture (also called multi-hop architecture) is a design pattern for organizing data in a lakehouse using three progressive layers:

• Bronze (Raw): Ingested data in its original format
• Silver (Refined): Cleansed and conformed data
• Gold (Curated): Business-level aggregates and features

When to Use This Skill

Use this skill when you need to:

• Design a new data pipeline with proper layering
• Migrate from traditional ETL to lakehouse architecture
• Implement incremental processing patterns
• Build a scalable data platform
• Ensure data quality at each layer

Architecture Principles

1. Bronze Layer (Raw)

Purpose: Store raw data exactly as received from source systems

Characteristics:

• Immutable historical record
• Schema-on-read approach
• Metadata enrichment (_ingested_at, _source_file)
• Minimal transformations
• Full audit trail

Use Cases:

• Data recovery
• Reprocessing requirements
• Audit compliance
• Debugging data issues

2. Silver Layer (Refined)

Purpose: Cleansed, validated, and standardized data

Characteristics:

• Schema enforcement
• Data quality checks
• Deduplication
• Standardization
• Type conversions
• Business rules applied

Use Cases:

• Downstream analytics
• Feature engineering
• Data science modeling
• Operational reporting

3. Gold Layer (Curated)

Purpose: Business-level aggregates optimized for consumption

Characteristics:

• Highly aggregated
• Optimized for queries
• Business KPIs
• Feature tables
• Production-ready datasets

Use Cases:

• Dashboards and BI
• ML model serving
• Real-time applications
• Executive reporting

Implementation Patterns

Pattern 1: Batch Processing

Bronze Layer:

def ingest_to_bronze(source_path: str, target_table: str):
    """Ingest raw data to Bronze layer."""
    df = (spark.read
        .format("cloudFiles")
        .option("cloudFiles.format", "parquet")
        .load(source_path)
        .withColumn("_ingested_at", current_timestamp())
        .withColumn("_source_file", input_file_name())
    )
    
    (df.write
        .format("delta")
        .mode("append")
        .option("mergeSchema", "true")
        .saveAsTable(target_table)
    )

Silver Layer:

def process_to_silver(bronze_table: str, silver_table: str):
    """Transform Bronze to Silver with quality checks."""
    bronze_df = spark.read.table(bronze_table)
    
    silver_df = (bronze_df
        .dropDuplicates(["id"])
        .filter(col("id").isNotNull())
        .withColumn("email", lower(trim(col("email"))))
        .withColumn("created_date", to_date(col("created_at")))
        .withColumn("quality_score", 
            when(col("email").rlike(r"^[\w\.-]+@[\w\.-]+\.\w+$"), 1.0)
            .otherwise(0.5)
        )
    )
    
    (silver_df.write
        .format("delta")
        .mode("overwrite")
        .saveAsTable(silver_table)
    )

Gold Layer:

def aggregate_to_gold(silver_table: str, gold_table: str):
    """Aggregate Silver to Gold business metrics."""
    silver_df = spark.read.table(silver_table)
    
    gold_df = (silver_df
        .groupBy("customer_segment", "region")
        .agg(
            count("*").alias("customer_count"),
            sum("lifetime_value").alias("total_ltv"),
            avg("quality_score").alias("avg_quality")
        )
        .withColumn("updated_at", current_timestamp())
    )
    
    (gold_df.write
        .format("delta")
        .mode("overwrite")
        .saveAsTable(gold_table)
    )

Pattern 2: Incremental Processing

Bronze (Streaming):

(spark.readStream
    .format("cloudFiles")
    .option("cloudFiles.format", "json")
    .load(source_path)
    .withColumn("_ingested_at", current_timestamp())
    .writeStream
    .format("delta")
    .option("checkpointLocation", checkpoint_path)
    .trigger(availableNow=True)
    .toTable(bronze_table)
)

Silver (Incremental Merge):

from delta.tables import DeltaTable

def incremental_silver_merge(bronze_table: str, silver_table: str, watermark: str):
    """Incrementally merge new Bronze data into Silver."""
    
    # Get new records since last watermark
    new_records = (spark.read.table(bronze_table)
        .filter(col("_ingested_at") > watermark)
    )
    
    # Transform
    transformed = transform_to_silver(new_records)
    
    # Merge into Silver
    silver = DeltaTable.forName(spark, silver_table)
    
    (silver.alias("target")
        .merge(
            transformed.alias("source"),
            "target.id = source.id"
        )
        .whenMatchedUpdateAll()
        .whenNotMatchedInsertAll()
        .execute()
    )

Data Quality Patterns

Quality Checks at Each Layer

Bronze:

• File completeness check
• Row count validation
• Schema drift detection

Silver:

• Null value checks
• Data type validation
• Business rule validation
• Referential integrity
• Duplicate detection

Gold:

• Aggregate accuracy
• KPI threshold checks
• Trend anomaly detection
• Completeness validation

Quality Check Implementation

def validate_silver_quality(table_name: str) -> Dict[str, bool]:
    """Run quality checks on Silver table."""
    df = spark.read.table(table_name)
    
    checks = {
        "no_null_ids": df.filter(col("id").isNull()).count() == 0,
        "valid_emails": df.filter(
            ~col("email").rlike(r"^[\w\.-]+@[\w\.-]+\.\w+$")
        ).count() == 0,
        "no_duplicates": df.count() == df.select("id").distinct().count(),
        "within_date_range": df.filter(
            (col("created_date") < "2020-01-01") |
            (col("created_date") > current_date())
        ).count() == 0
    }
    
    return checks

Optimization Strategies

Bronze Layer Optimization

-- Partition by ingestion date
CREATE TABLE bronze.raw_events
USING delta
PARTITIONED BY (ingestion_date)
AS SELECT *, current_date() as ingestion_date FROM source;

-- Enable auto-optimize
ALTER TABLE bronze.raw_events
SET TBLPROPERTIES (
    'delta.autoOptimize.optimizeWrite' = 'true',
    'delta.autoOptimize.autoCompact' = 'true'
);

Silver Layer Optimization

-- Z-ORDER for common filters
OPTIMIZE silver.customers
ZORDER BY (customer_segment, region, created_date);

-- Enable Change Data Feed
ALTER TABLE silver.customers
SET TBLPROPERTIES (delta.enableChangeDataFeed = true);

Gold Layer Optimization

-- Liquid clustering for query performance
CREATE TABLE gold.customer_metrics
USING delta
CLUSTER BY (customer_segment, date)
AS SELECT * FROM aggregated_metrics;

-- Optimize and vacuum
OPTIMIZE gold.customer_metrics;
VACUUM gold.customer_metrics RETAIN 168 HOURS;

Complete Example

See /​templates/​bronze-silver-gold/ for a complete implementation including:

• Project structure
• Bronze ingestion scripts
• Silver transformation logic
• Gold aggregation queries
• Data quality tests
• Deployment configuration

Best Practices

1. Idempotency: Ensure pipelines can be re-run safely
2. Incrementality: Process only new/​changed data
3. Quality Gates: Block bad data from progressing
4. Schema Evolution: Handle schema changes gracefully
5. Monitoring: Track pipeline health and data quality
6. Documentation: Document data lineage and transformations
7. Testing: Unit test transformations, integration test pipelines

Common Pitfalls to Avoid

❌ Don't:

• Mix transformation logic across layers
• Skip Bronze layer to "save storage"
• Over-aggregate too early
• Ignore data quality in Silver
• Hard-code business logic in Bronze

✅ Do:

• Keep Bronze immutable
• Enforce quality in Silver
• Optimize Gold for consumption
• Use incremental processing
• Implement proper monitoring

Related Skills

• delta-live-tables: Declarative pipeline orchestration
• data-quality: Great Expectations integration
• testing-patterns: Pipeline testing strategies
• cicd-workflows: Deployment automation

References

• Databricks Medallion Architecture
• Delta Lake Best Practices