Case Study: Real Estate Analytics achieves 50x faster data pipelines with ClickHouse

How Real Estate Analytics made its data pipeline 50x faster with ClickHouse

Real Estate Analytics (REA) is a Singapore-founded property data company operating across Australia, Hong Kong, and Malaysia. As its datasets grew to more than 200 columns and up to 20 million rows, REA’s MongoDB-based architecture struggled with large aggregations, frequent updates, and slow table refreshes, making it difficult to deliver real-time insights.

Real Estate Analytics implemented ClickHouse to replace bottlenecks in its analytical workload and redesign its data pipeline. With ClickHouse, aggregation queries ran 10x faster and full table refreshes dropped from up to 30 minutes to under 30 seconds, a 50x improvement. ClickHouse also helped REA streamline ingestion and transformation by reducing intermediate ETL layers, lowering development effort and infrastructure costs.

Real Estate Analytics

Yi Sam Lee

Data Lead