SCALABLE REAL-TIME INDEXING IN ELASTICSEARCH FOR LARGE-VOLUME REAL ESTATE DATA

With the proliferation of digital real estate platforms, the volume and complexity of property-related data have increased exponentially. Efficiently indexing and retrieving this data in real time has become essential for enabling responsive user experiences, analytics, and automation in PropTech systems. Elasticsearch, a distributed search engine built on Apache Lucene, has become a foundational tool for supporting these needs due to its scalability, high availability, and near real-time performance. This review provides a comprehensive analysis of scalable real-time indexing architectures using Elasticsearch, focusing on their application to large-volume real estate datasets. We present a theoretical model of indexing, experimental results comparing different indexing strategies, and case studies from real-world implementations. Furthermore, we identify performance bottlenecks, propose architectural improvements, and outline future research directions involving AI integration, geo-spatial enhancements, and serverless deployment. Our findings suggest that Elasticsearch, when properly tuned and architected, is highly suitable for the dynamic requirements of real estate platforms. The review aims to serve both as a technical guide and a scholarly resource for researchers and practitioners in the domains of data engineering, search systems, and property technology.