Modern Lakehouse Architecture Patterns

The lakehouse architecture combines the best of data lakes and data warehouses. Let’s explore proven patterns.

Medallion Architecture

The medallion architecture is a data design pattern used to logically organize data in a lakehouse.

Bronze Layer (Raw)

• Ingests data in its original format
• Minimal transformations
• Complete history preserved

Silver Layer (Cleansed)

• Validated and cleaned data
• Deduplicated
• Conformed to standard schemas

Gold Layer (Curated)

• Business-level aggregations
• Optimized for analytics
• High performance

# Bronze to Silver transformation
df_bronze = spark.read.format("delta").load("/bronze/events")

df_silver = df_bronze \
  .dropDuplicates(["event_id"]) \
  .filter(col("event_time").isNotNull()) \
  .withColumn("processed_at", current_timestamp())

df_silver.write.format("delta").mode("append").save("/silver/events")

Data Mesh Principles

1. Domain-oriented ownership
2. Data as a product
3. Self-serve data infrastructure
4. Federated computational governance

Streaming Architecture

For real-time use cases:

• Structured Streaming with Delta Lake
• Auto Loader for incremental ingestion
• Change Data Capture (CDC) patterns

Governance Layer

Built-in governance features:

• Unity Catalog for metadata
• Fine-grained access control
• Data lineage tracking
• Audit logs

Conclusion

A well-designed lakehouse architecture provides flexibility, scalability, and governance. Start with medallion architecture and evolve based on your organization’s needs.