In high-scale real estate platforms, the real challenge isn’t just about fetching listings, it’s about processing and serving millions of records across diverse MLS (Multiple Listing Service) providers with speed, resilience, and cost-efficiency. Whether you’re integrating with the United States’ BrightMLS or Canada’s TRREB, each MLS comes with its own quirks: varying data models, inconsistent metadata, irregular update cycles, and evolving schemas.
At scale, those differences are edge cases no longer; they’re daily roadblocks. Your backend must not only ingest and normalize terabytes’ worth of listing data but also cope with real-time sync, deduplication, tagging, scoring, and advanced filtering, bottleneck-free. For our team at a high-flying Vancouver-based real estate startup, the answer was to design a system in Go (Golang) that would tolerate high concurrency, have hard memory limits, and operate under realistic production pressure.