Build a Property Pricing Data Warehouse with DuckDB and Python
Group: Capstone Project
Product Category: Cloud & Data Engineering
Sub Category: Data Modeling & Dimensional Modeling
Solution Available
Source code, datasets, and other related files for the solution will be provided.
About this Product
Build a Property Pricing Data Warehouse with DuckDB and Python is a data engineering product that builds CompVantage — a governed property pricing lakehouse for a fictional real estate analytics firm — integrating three source feeds into a dimensionally modeled star schema with Type 2 property history, PII masking, and automated data quality rules. No Docker, no server, no cloud required.
With this product, you'll build a pipeline and warehouse that can:
- Land raw records into Bronze Parquet, validate and mask PII before Silver, and reject invalid rows with a reason code
- Maintain a Type 2 dim_property dimension — inserting on first appearance and versioning on any tracked attribute change
- Curate three fact tables — fact_listing, fact_valuation, and fact_market_occupancy — using idempotent upserts
- Run an automated data quality rule engine with non-blocking governance exceptions logged separately
- Publish four SQL reporting views — comparable-sales summary, valuation coverage, market trend, and property valuation history
Building this product gives you hands-on experience in:
- Dimensional modeling — star schema with conformed dimensions, Type 2 SCD, and three fact tables
- Medallion architecture — Bronze → Silver → Gold with Parquet data lake and embedded DuckDB
- Data governance
- Pipeline orchestration — stage ordering, JSON checkpoint, retry with backoff, and service-key authentication
It uses Python, DuckDB, Parquet, pandas, python-dotenv, and pytest
Why this product matters: Dimensional modeling, SCD Type 2, and data governance are three of the most tested skills in data engineering interviews — all three taught together in a real estate domain PropTech employers immediately recognise.
Resources
The Build a Property Pricing Data Warehouse with DuckDB and Python setup and execution guide is available as a Read-Only PDF. It provides step-by-step instructions for setting up the project, installing dependencies, running the pipeline, and executing the tests.
Guide Contents:
- Prerequisites
- Virtual environment setup and dependency installation from
requirements.txt .envsetup from.env.examplewith warehouse path and service-key configurationsetupcommand to create DuckDB warehouse, gold schema DDL, and seed conformed dimensions- Commands to run the pipeline
- Commands to run the pytest suite covering all 29 acceptance criteria
- Expected output
- Common issues
Topics
Skills
Languages
English
Level
Intermediate
Business Domain
Real Estate & PropTech
Project Mentors
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