Real Estate Platform with ETL & Scraping
A Django application built on aggregated property data from multiple websites. Data was collected through scraping and APIs, put through an ETL process, and then surfaced through the Django site.
- Year
- 2024
- Engagement
- Backend & data engineering
- Focus
- Backend, Data Engineering, Full Stack
- Status
- Open source
01 — The problem
Property listings live on many sites in many shapes. Comparing them requires collecting them first, and then reconciling formats that agree on nothing.
02 — Our solution
A collection layer using scraping and APIs, an ETL stage that normalises everything into one schema, and a Django application that serves the unified result.
How it works
The pipeline, step by step — from the first input to the final output.
- 01
Collection
Listings are gathered from multiple sources via scraping and APIs.
- 02
Transform
ETL normalises differing formats into a single schema.
- 03
Storage
Clean records are persisted in the application database.
- 04
Application
Django serves browsing and search over the unified dataset.
What we built
The outcomes
01
One browsable dataset assembled from many sources
02
A repeatable collection-to-application pipeline
Challenges we solved
Every source disagrees
Field names, units and completeness vary per site. The transform stage carries most of the complexity.
Technology stack
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