{"id":812,"date":"2025-08-31T11:43:43","date_gmt":"2025-08-31T11:43:43","guid":{"rendered":"https:\/\/dataopsschool.com\/blog\/?p=812"},"modified":"2025-08-31T11:43:44","modified_gmt":"2025-08-31T11:43:44","slug":"databricks-medallion-architecture-in-data-lakehouse","status":"publish","type":"post","link":"https:\/\/dataopsschool.com\/blog\/databricks-medallion-architecture-in-data-lakehouse\/","title":{"rendered":"Databricks: Medallion Architecture in Data Lakehouse"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Here\u2019s a step-by-step tutorial with deep explanations + examples<\/strong>:<\/p>\n\n\n\n

\n\n\n\n

\ud83d\udcd8 Medallion Architecture in Data Lakehouse<\/h1>\n\n\n\n

(Bronze, Silver, Gold Layers with Databricks)<\/em><\/p>\n\n\n\n

\n\n\n\n

1. \ud83d\udd39 Introduction<\/h2>\n\n\n\n

In a Data Lakehouse<\/strong> (e.g., on Databricks<\/strong>), data pipelines often deal with raw, semi-processed, and business-ready datasets<\/strong>.
To organize this systematically, Databricks (and the Lakehouse paradigm) uses the Medallion Architecture<\/strong>:<\/p>\n\n\n\n

    \n
  • Bronze \u2192 Silver \u2192 Gold layers<\/strong><\/li>\n\n\n\n
  • Each layer adds refinement, cleaning, and value<\/strong>.<\/li>\n\n\n\n
  • Multi-hop design ensures traceability, scalability, and governance<\/strong>.<\/li>\n<\/ul>\n\n\n\n

    Think of it as:<\/p>\n\n\n\n

      \n
    • Bronze = Raw ingestion<\/strong><\/li>\n\n\n\n
    • Silver = Cleansed \/ standardized<\/strong><\/li>\n\n\n\n
    • Gold = Business consumption<\/strong><\/li>\n<\/ul>\n\n\n\n
      \"\"<\/figure>\n\n\n\n
      \n\n\n\n

      2. \ud83d\udd39 What is Medallion Architecture?<\/h2>\n\n\n\n

      The Medallion Architecture<\/strong> is a multi-hop pipeline design<\/strong>:<\/p>\n\n\n\n

      \u27a1 Data flows left to right<\/strong>
      \u27a1 Gets cleaner, more refined, and enriched at each stage<\/strong>
      \u27a1 Supports auditability, lineage, and governance<\/strong><\/p>\n\n\n\n

      Diagram (Conceptual)<\/h3>\n\n\n\n
      Source Systems (files, APIs, streams)\n         \u2193\n     Bronze Layer (Raw)\n         \u2193\n     Silver Layer (Cleansed \/ Enriched)\n         \u2193\n     Gold Layer (Aggregated \/ Business-ready)\n         \u2193\n   BI Reports | ML Models | Data Sharing\n<\/code><\/pre>\n\n\n\n
      \n\n\n\n
      3. \ud83d\udd39 Bronze Layer<\/h2>\n\n\n\n
      \ud83d\udccc Purpose:<\/h3>\n\n\n\n
        \n
      • Store raw ingested data<\/strong> exactly \u201cas is\u201d from source systems.<\/li>\n\n\n\n
      • Acts as a single source of truth<\/strong> for history\/audit.<\/li>\n\n\n\n
      • Supports batch, streaming, API ingestion<\/strong>.<\/li>\n<\/ul>\n\n\n\n
        Example Sources:<\/h3>\n\n\n\n
          \n
        • CSV, JSON, Parquet, Avro<\/li>\n\n\n\n
        • Event hubs \/ Kafka \/ IoT streams<\/li>\n\n\n\n
        • RDBMS snapshots<\/li>\n<\/ul>\n\n\n\n
          Databricks Example (Bronze Table Creation):<\/h3>\n\n\n\n
          -- Ingest raw CSV files into Bronze layer\nCREATE TABLE bronze_sales\nUSING DELTA\nLOCATION '\/mnt\/raw\/bronze\/sales'\nAS\nSELECT *, current_timestamp() AS ingestion_time\nFROM csv.`\/mnt\/data\/raw\/sales\/*.csv`;\n<\/code><\/pre>\n\n\n\n
          \u2705 Notes:<\/p>\n\n\n\n
            \n
          • bronze_sales<\/code> keeps raw data<\/strong>.<\/li>\n\n\n\n
          • Added ingestion_time<\/code> for audit & lineage<\/strong>.<\/li>\n<\/ul>\n\n\n\n
            \n\n\n\n
            4. \ud83d\udd39 Silver Layer<\/h2>\n\n\n\n
            \ud83d\udccc Purpose:<\/h3>\n\n\n\n
              \n
            • Take raw bronze data<\/strong> and refine it<\/strong>:\n
                \n
              • Deduplicate<\/strong><\/li>\n\n\n\n
              • Filter bad records<\/strong><\/li>\n\n\n\n
              • Apply standard schema<\/strong><\/li>\n\n\n\n
              • Join with other datasets<\/strong><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
                Operations:<\/h3>\n\n\n\n
                  \n
                • Remove null values<\/li>\n\n\n\n
                • Standardize column names<\/li>\n\n\n\n
                • Join lookup tables<\/li>\n\n\n\n
                • Handle schema evolution<\/li>\n<\/ul>\n\n\n\n
                  Databricks Example (Silver Table Creation):<\/h3>\n\n\n\n
                  -- Create Silver layer with cleaned sales data\nCREATE OR REPLACE TABLE silver_sales AS\nSELECT DISTINCT\n    CAST(sale_id AS STRING) AS sale_id,\n    customer_id,\n    CAST(amount AS DOUBLE) AS amount,\n    to_date(sale_date, 'yyyy-MM-dd') AS sale_date,\n    ingestion_time\nFROM bronze_sales\nWHERE amount IS NOT NULL;\n<\/code><\/pre>\n\n\n\n
                  \u2705 Notes:<\/p>\n\n\n\n
                    \n
                  • Duplicate sales removed with DISTINCT<\/code>.<\/li>\n\n\n\n
                  • Schema cleaned (amount \u2192 DOUBLE<\/code>, sale_date \u2192 DATE<\/code>).<\/li>\n\n\n\n
                  • Filtering out invalid\/null records<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n
                    5. \ud83d\udd39 Gold Layer<\/h2>\n\n\n\n
                    \ud83d\udccc Purpose:<\/h3>\n\n\n\n
                      \n
                    • Provide business-ready datasets<\/strong> for:\n
                        \n
                      • BI Dashboards (PowerBI, Tableau, Databricks SQL)<\/li>\n\n\n\n
                      • ML Models<\/li>\n\n\n\n
                      • External data sharing<\/li>\n<\/ul>\n<\/li>\n\n\n\n
                      • Data is aggregated, enriched, and business-friendly<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                        Typical Transformations:<\/h3>\n\n\n\n
                          \n
                        • Aggregations (SUM, AVG, COUNT)<\/li>\n\n\n\n
                        • Derived KPIs (Revenue, Growth, Retention)<\/li>\n\n\n\n
                        • Star\/Snowflake schemas<\/li>\n<\/ul>\n\n\n\n
                          Databricks Example (Gold Table Creation):<\/h3>\n\n\n\n
                          -- Create Gold table for business reports\nCREATE OR REPLACE TABLE gold_sales_summary AS\nSELECT\n    customer_id,\n    SUM(amount) AS total_spent,\n    COUNT(sale_id) AS total_transactions,\n    MAX(sale_date) AS last_purchase_date\nFROM silver_sales\nGROUP BY customer_id;\n<\/code><\/pre>\n\n\n\n
                          \u2705 Notes:<\/p>\n\n\n\n
                            \n
                          • Gold layer is aggregated<\/strong> by customer_id<\/code>.<\/li>\n\n\n\n
                          • Optimized for BI dashboards & reporting<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                            \n\n\n\n
                            6. \ud83d\udd39 End-to-End Data Flow<\/h2>\n\n\n\n
                              \n
                            1. Raw ingestion \u2192 Bronze<\/strong>
                              (Keep data unchanged, add audit columns)<\/li>\n\n\n\n
                            2. Refine \u2192 Silver<\/strong>
                              (Clean, deduplicate, enforce schema)<\/li>\n\n\n\n
                            3. Business aggregation \u2192 Gold<\/strong>
                              (KPIs, analytics-ready data)<\/li>\n<\/ol>\n\n\n\n
                              \n\n\n\n
                              7. \ud83d\udd39 Data Quality & Governance<\/h2>\n\n\n\n
                                \n
                              • Apply data validation rules<\/strong> in Silver.<\/li>\n\n\n\n
                              • Use Unity Catalog<\/strong> for governance across all layers.<\/li>\n\n\n\n
                              • Track lineage<\/strong>: Gold \u2192 Silver \u2192 Bronze \u2192 Source.<\/li>\n\n\n\n
                              • Implement audit logs<\/strong> with ingestion_time<\/code> and metadata columns.<\/li>\n<\/ul>\n\n\n\n
                                \n\n\n\n
                                8. \ud83d\udd39 Why Medallion Architecture?<\/h2>\n\n\n\n
                                  \n
                                • Scalability<\/strong>: Supports millions of records.<\/li>\n\n\n\n
                                • Flexibility<\/strong>: Same raw data \u2192 multiple refined use cases.<\/li>\n\n\n\n
                                • Auditability<\/strong>: Raw always available.<\/li>\n\n\n\n
                                • Separation of Concerns<\/strong>: Each layer has clear purpose.<\/li>\n\n\n\n
                                • Performance<\/strong>: Optimized Delta format + Z-order indexing.<\/li>\n<\/ul>\n\n\n\n
                                  \n\n\n\n
                                  \u2705 Summary<\/h2>\n\n\n\n
                                    \n
                                  • Bronze Layer<\/strong> = Raw, historical, unmodified data.<\/li>\n\n\n\n
                                  • Silver Layer<\/strong> = Refined, cleansed, schema-standardized data.<\/li>\n\n\n\n
                                  • Gold Layer<\/strong> = Business-ready aggregated data.<\/li>\n\n\n\n
                                  • Together, they form a multi-hop pipeline<\/strong> that is robust, scalable, and traceable.<\/li>\n<\/ul>\n\n\n\n
                                    \n\n\n\n
                                    \ud83d\udd25 Pro Tip:
                                    In real-world Databricks projects, you\u2019ll often implement this pipeline using Workflows\/Jobs<\/strong>, Auto Loader for ingestion<\/strong>, and Delta Live Tables (DLT)<\/strong> for automated Medallion pipelines<\/strong>.<\/p>\n\n\n\n
                                    \n\n\n\n
                                    <\/p>\n","protected":false},"excerpt":{"rendered":"
                                    Here\u2019s a step-by-step tutorial with deep explanations + examples: \ud83d\udcd8 Medallion Architecture in Data Lakehouse (Bronze, Silver, Gold Layers with Databricks) 1. \ud83d\udd39 Introduction In a Data… <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-812","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/812","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=812"}],"version-history":[{"count":1,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/812\/revisions"}],"predecessor-version":[{"id":818,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/812\/revisions\/818"}],"wp:attachment":[{"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=812"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=812"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}