This is a meaty one if you've never thought about real estate data at scale.
Start by clarifying functional and non-functional requirements, then design a high-level architecture that separates read-heavy search traffic from write-heavy listing ingestion. Focus on data modeling for properties, geo-spatial indexing for search, and trade-offs between consistency, latency, and cost.
Pro tip: Emphasize how Zillow aggregates data from multiple sources (MLS, public records, user submissions) and the need for a robust deduplication and reconciliation pipeline. Also, discuss how to handle stale data and ensure freshness for high-demand listings.
Ask about scale (e.g., number of listings, QPS), key features (search, filters, map view, alerts), and non-functional needs (latency, availability, consistency).
Sketch a system with separate services for data ingestion, search, and user-facing APIs. Use a CDN for static assets and a load balancer for API traffic.
Design a property schema with attributes like location, price, beds/baths, and media. Choose appropriate databases: relational for transactions, NoSQL for flexible attributes, and a search engine (e.g., Elasticsearch) for full-text and geo queries.
Explain how to index properties for fast retrieval, including geo-spatial indexing (e.g., geohash, R-tree) and support for filters, sorting, and pagination. Discuss caching strategies for popular queries.
Describe the pipeline for ingesting data from multiple sources, deduplication, and reconciliation. Address eventual consistency and how to handle updates and deletions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.