Databricks Bronze Silver Gold


        Databricks Bronze Silver Gold

  • 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.


    • 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


    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.

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