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Real Estate Technology

Real Estate MLS Data Collection & Normalization

Built a scalable MLS ingestion and normalization pipeline handling data from 50+ providers, improving throughput by 60% and reducing duplicates by 90%.

8 full-stack developers team members
Real Estate Technology
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Project Overview

Developed a Rails-based ETL pipeline that normalized data from 50+ MLS providers using RETS standards. Sidekiq background jobs ensured high-volume ingestion without performance issues. ElasticSearch centralized indexed property data for sub-second searches. Deduplication algorithms reduced duplicates by 90%, and legacy systems (Rails + Ember + React) were unified into a seamless platform.

Key Challenges

  • MLS data integration from 50+ different providers
  • Throughput scaling and duplicate listing resolution
  • Legacy system unification (Rails + Ember + React)
  • Transitioning legacy APIs to modern platforms
  • Delivering consistent high-quality IDX data

Technologies & Solutions

Ruby on Rails ETL framework Sidekiq background jobs ElasticSearch for indexing and fast queries MLS RETS standard for data ingestion Deduplication algorithms React.js and Ember.js integration

Key Metrics

60% MLS throughput improvement
90% reduction in duplicate listings
400 agent/broker sites optimized
Double-digit traffic increases from higher data quality

Results & Impact

60% MLS throughput improvement, 90% duplicate reduction, consistent IDX data quality across 400+ sites.

Real Estate MLS Data Collection & Normalization

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