If you're building:
- Real estate data platform
- IDX / MLS aggregation system
- Scalable listing infrastructure
This system is designed to eliminate provider fragmentation and image processing bottlenecks.
A production-grade, provider-agnostic real estate data infrastructure system.
Built for scaling MLS/IDX data ingestion across multiple providers with:
- High-frequency incremental sync
- Normalized canonical schema
- Async image processing pipeline (WebP optimized)
- CDN-ready architecture
Real estate data providers (Bridge, Trestle, MLSGrid, etc.) all return:
- different schemas
- different rate limits
- inconsistent media handling
- duplicate / dirty data issues
This system solves it by introducing:
A unified ingestion + normalization + media optimization layer.
MLS Providers ↓ Adapter Layer (Provider Abstraction) ↓ Normalization Engine (Canonical Schema) ↓ PostgreSQL Database ↓ Celery Queue System ↓ WebP Image Processor ↓ CDN Storage Layer
Plug-and-play adapters for:
- Bridge Interactive
- Trestle
- MLSGrid
- RESO Web API
- PostgreSQL normalized schema
- JSONB raw payload preservation
- Celery distributed workers
- WebP compression optimization
- CDN-ready image delivery
- Incremental sync (timestamp-based)
- Queue-based processing
- Stateless worker design
- Python 3.11
- PostgreSQL + SQLAlchemy
- Celery + Redis
- Pillow (WebP compression)
- REST / RESO Web API
git clone https://github.com/daviediao-code/universal-idx-pipeline
cd universal-idx-pipeline
pip install -r requirements.txt
python core/pipeline.py
📊 Example Output
[SYS] Connecting to Bridge Interactive...
[SYS] Mapping MLS-Bridge-9981
[SYS] Queueing images for WebP conversion...
[SYS] Sync complete
🎯 Use Cases
IDX real estate platforms
MLS data aggregation systems
Property listing engines
Media-heavy data pipelines
📌 Why This Matters
This architecture is designed for:
high-volume real estate data
multi-provider integration
scalable SaaS platforms
production-grade ingestion pipelines