Cloud-based Real Estate Data Warehouse Using Databricks Medallion Architecture: A Complete Practice from Raw Data to RAG Intelligent Assistant

This article introduces a production-grade real estate data analysis platform that uses the Databricks Medallion Architecture (Bronze/Silver/Gold) and PySpark to build an end-to-end data engineering pipeline. It implements layered data cleaning, transformation, and modeling to form a star-schema data warehouse, and plans to integrate RAG technology to provide conversational intelligent insights. The core tech stack includes Databricks, Delta Lake, Unity Catalog, etc.

In the data-driven real estate industry, traditional data processing faces challenges such as inconsistent data quality, hard-to-scale architecture, and inability to support advanced AI applications. This project provides a complete data platform covering data ingestion, cleaning/transformation, and building an optimized data warehouse, with a forward-looking plan for integration with generative AI (RAG).

• Databricks: Unified cloud-native data analysis platform
• PySpark: Large-scale distributed data processing
• Delta Lake: ACID transaction support and data version control
• Unity Catalog: Unified data governance and access control
• Power BI: Business intelligence visualization
• RAG (Retrieval-Augmented Generation): Planned AI conversation layer

Bronze Layer: Raw Data Ingestion

Preserves the original Parquet data format, iteratively handles schema mismatches, and stores data in the workspace.default.real_estate_bronze table to ensure no loss of original information.

Silver Layer: Data Cleaning and Standardization

Performs categorical variable standardization, regex parsing (JSON/developer name/date/currency), layered median imputation for missing values, and feature engineering (e.g., payment flexibility score).

Gold Layer: Star-Schema Data Warehouse

Builds dimension tables (dim_date/dim_location/dim_developer/dim_property) and fact tables (fact_sales), and ensures data integrity by registering primary and foreign key constraints via Unity Catalog.

Plans to use Gold layer data combined with Databricks Vector Search technology to implement vector embedding generation and efficient retrieval index construction, deploy a conversational AI assistant, provide intelligent decision support for real estate agents, investors, and homebuyers, and enable seamless integration of structured data warehouses with generative AI applications.

Implementation Steps

Execute the notebooks in order: 01_Bronze_Ingestion.py → 02_Silver_Cleansing.py → 03_Gold_DWH.py → 04_Gold_Constraints.py, or use Databricks Workflows to orchestrate automated scheduling.

Environment Requirements

• Databricks Workspace (with Unity Catalog enabled)
• Compute cluster: Databricks Runtime 13.0+
• Raw real estate Parquet files preloaded into the specified Databricks volume

This project provides references for data engineers and architects:

• Architectural Normativity: Follows Databricks best practices, with clear and maintainable code
• Data Quality First: Multi-layered cleaning and constraints ensure data quality
• Scalability: Modular design supports independent evolution and integration of new data sources
• AI Readiness: Forward-looking RAG design enables a smooth transition to the AI-driven analysis era It is a reference implementation for teams planning or optimizing data platforms.