Part - 2 | Messy Data → Clean Data in Databricks 🔥 Silver Layer Tutorial

Silver Layer in Databricks | Data Cleaning & Transformation | Retail Project In this video, we build the Silver Layer for our End-to-End Retail Data Engineering Project using Medallion Architecture in Databricks. After ingesting raw data in the Bronze layer, the next step is transforming messy datasets into clean, reliable, analytics-ready tables. In this tutorial, we clean and transform both Retail Orders and Retail Customers datasets. You will learn how to: ✅ Remove duplicate records ✅ Convert raw string columns to correct data types ✅ Clean and standardize messy data ✅ Validate email formats ✅ Handle null and invalid values ✅ Parse date columns correctly ✅ Create trusted Silver Delta tables Datasets used in this project: 📦 Retail Orders Dataset 👥 Retail Customers Dataset The Silver layer ensures high data quality and consistency before building business-level models in the Gold layer. This project follows the industry standard Medallion Architecture: 🔹 Bronze Layer – Raw Data Ingestion 🔹 Silver Layer – Data Cleaning & Validation 🔹 Gold Layer – Business Metrics & Analytics By the end of this tutorial series, you will understand how modern Lakehouse data platforms are built using Databricks and Delta Lake. This series is ideal for: ✔ Data Engineering beginners ✔ Databricks learners ✔ Azure Data Engineers ✔ Spark developers ✔ Interview preparation ✔ Real portfolio projects 📌 Next Video: Gold Layer – Business Aggregations & Fact Tables Resource - https://github.com/dataworldsolution/DatabricksTutorial/blob/main/End%20to%20End%20Databricks%20Retail%20Sales%20Project/Part-2%20End%20to%20End%20Project%20-%20Retail%20Sales%20Analysis.ipynb #databricks #datapipelines #dataengineering #pyspark #python