REA, a real estate analytics company, struggled with its MongoDB-based architecture due to large datasets and performance issues. They turned to ClickHouse, which offered improved query performance, faster table refreshes, and more flexibility in handling large-scale data updates. REA redesigned their pipeline to pull data directly into ClickHouse, reducing intermediate processing layers and improving efficiency. While ClickHouse has solved many of REA's challenges, there are some gaps in its support for historical dates and spatial data, as well as a lack of native support for automatically incrementing IDs. Despite these challenges, REA has seen significant improvements in query performance and pipeline efficiency with ClickHouse, enabling them to provide real-time insights to customers and scale their operations.