✅ Raw → Bronze → Silver → Gold Architecture 1. How to organize data in a scalable lakehouse 2. Ingest data into the Raw layer 3. Clean & standardize into Bronze 4. Apply transformations & deduplication in Silver 5. Build analytics-ready Gold tables ✅ Slowly Changing Dimensions (SCD) in PySpark 1. SCD Type 1 - Overwrite old data - Fast dimension updates 2. SCD Type 2 - Preserve full history -Track versioning using: . effective_date . end_date . is_current flag 📘 What This Video Covers - Writing clean PySpark ETL code - Implementing both Type 1 and Type 2 dimension updates - Validating Gold layer tables for analytics 🔧 Tech Stack - Apache Spark / PySpark - Databricks Notebook - Medallion Architecture (Raw/Bronze/Silver/Gold) ⭐ If you find this helpful, please LIKE, SHARE & SUBSCRIBE