The latest articles on Forem by Khandare Shubham (@khandare_shubham_4d9ec230). https://forem.com/khandare_shubham_4d9ec230 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3671200%2Fc3d2ccd7-7560-4a6d-9569-8a42b0b4fd71.png https://forem.com/khandare_shubham_4d9ec230 en Khandare Shubham Fri, 19 Dec 2025 19:15:53 +0000 https://forem.com/khandare_shubham_4d9ec230/how-i-built-an-end-to-end-etl-pipeline-using-databricks-delta-lake-45nc https://forem.com/khandare_shubham_4d9ec230/how-i-built-an-end-to-end-etl-pipeline-using-databricks-delta-lake-45nc <p>In this project, I built an end-to-end ETL pipeline using Databricks and Delta Lake,<br> following the Bronze–Silver–Gold architecture.</p> <p>The goal was to simulate a real-world data engineering pipeline with incremental<br> processing, workflow orchestration, and analytics-ready datasets.</p> <h3> Tech Stack </h3> <ul> <li>Databricks (Free Edition)</li> <li>Apache Spark (PySpark)</li> <li>Delta Lake</li> <li>SQL</li> <li>GitHub</li> </ul> <h3> Architecture Overview </h3> <p>The pipeline follows the Bronze–Silver–Gold data architecture:</p> <ul> <li> <strong>Bronze Layer</strong>: Raw data ingestion (append-only)</li> <li> <strong>Silver Layer</strong>: Cleaned and deduplicated data with incremental updates using Delta MERGE</li> <li> <strong>Gold Layer</strong>: Aggregated business metrics optimized for analytics</li> </ul> <h3> Architecture Overview </h3> <p>The pipeline follows the Bronze–Silver–Gold data architecture:</p> <ul> <li> <strong>Bronze Layer</strong>: Raw data ingestion (append-only)</li> <li> <strong>Silver Layer</strong>: Cleaned and deduplicated data with incremental updates using Delta MERGE</li> <li> <strong>Gold Layer</strong>: Aggregated business metrics optimized for analytics</li> </ul> <h3> Bronze Layer </h3> <p>The Bronze layer ingests raw CSV data into Delta tables in append mode.<br> This layer acts as the source of truth and allows full reprocessing if downstream<br> transformations fail.</p> <h3> Silver Layer </h3> <p>The Silver layer performs data cleaning and deduplication.<br> Incremental updates are handled using Delta Lake MERGE to ensure idempotent processing<br> and avoid duplicate records.</p> <h3> Gold Layer </h3> <p>The Gold layer contains aggregated business metrics such as:</p> <ul> <li>Daily sales KPIs</li> <li>Customer-level metrics</li> <li>Product-level metrics</li> </ul> <p>Gold tables are rebuilt using overwrite mode to ensure consistent and deterministic results.</p> <h3> Workflow Orchestration </h3> <p>The entire pipeline is orchestrated using Databricks Workflows.<br> Tasks are executed in sequence from Bronze to Silver, followed by parallel Gold aggregations.</p> <h3> Source Code </h3> <p>The complete source code is available on GitHub:<br> <a href="https://github.com/shubhkhandare/databricks-etl-sales" rel="noopener noreferrer">https://github.com/shubhkhandare/databricks-etl-sales</a></p> <h3> Conclusion </h3> <p>This project helped me understand how production-style ETL pipelines are designed<br> using Databricks and Delta Lake, including incremental processing and workflow orchestration.</p> databricks sql pyspark etl