Here’s a breakdown of the Bronze, Silver, and Gold layers in a Databricks Medallion architecture, including their purposes and common transformations:

Medallion Architecture Overview

The Medallion Architecture is a popular data organization pattern for data lakes and lakehouses, particularly on the Databricks platform. It’s designed to refine data into cleaner, more trustworthy datasets progressively.

Layers

• Bronze:
  • Purpose: Ingest raw, unprocessed data from various sources (structured, semi-structured, unstructured).
  • Data Quality: Limited data cleaning or transformations. Maintains historical data for potential reprocessing.
  • Use Cases:  Initial data exploration archiving for regulatory or compliance purposes.
• Silver:
  • Purpose: Cleanse, validate, and transform data from the Bronze layer. Implement basic quality checks.
  • Data Quality:  Data is reliable, consistent, and ready for downstream analytics.
  • Use Cases: Reporting, dashboards, basic machine learning modeling.
    • Typical Transformations: Standardization (e.g., date/time formats, units of measurement)
    • Filtering out invalid or erroneous records
    • Joining data from multiple sources
    • De-duplicating records
    • Basic business logic
• Gold:
  • Purpose: Generate business-level aggregates and features optimized for analytics and reporting.
  • Data Quality: Highest quality data, highly reliable for making business decisions.
  • Use Cases: Advanced analytics, machine learning, dashboards, insights for stakeholders
    • Typical Transformations: Aggregations (e.g., sums, averages, counts)
    • Complex business logic and calculations
    • Feature engineering for machine learning

Example

Imagine data coming from IoT sensors:

• Bronze: Raw JSON sensor readings, potentially with inconsistencies, errors, and missing data.
• Silver: Cleaned data – standardized timestamps, filtered out wrong readings, potentially joined with device metadata.
• Gold:  Hourly/daily aggregations of sensor readings per device, along with derived features (e.g., variance over time) suitable for anomaly detection.

Key Benefits

• Data Quality and Traceability:  Progressive improvement and a clear lineage of transformations
• Data Governance:   Enforces consistency, helping maintain compliance & regulatory requirements.
• Scalability:   Handles large volumes of data efficiently
• Performance:  Optimized for specific queries in the Gold layer.

Tools in Databricks

• Delta Live Tables (DLT): Simplifies building reliable, declarative ETL pipelines for creating and managing Bronze, Silver, and Gold tables.
• Apache Spark:  The core computational engine for data processing within Databricks.
• Databricks SQL: For interactive exploration and analysis across layers.