The Challenge
Skythena needed a platform to aggregate real estate data from multiple sources, map complex property relationships (ownership chains, geographic proximity, market comparisons), and serve analytics through REST APIs. The data requirements demanded both flexible document storage for varied property attributes and graph-based relationship modeling for interconnected property networks.
Our Approach
Daiviksoft built a multi-database analytics platform using MongoDB for flexible document storage of property data and Neo4J as a graph database for relationship mapping between properties, owners, and market segments. The system runs on multiple AWS EC2 instances — separate services for the API layer, MongoDB access, and Neo4J graph queries — providing scalable, purpose-built data access for different query patterns.
Key Deliverables
- Multi-database architecture: MongoDB for property documents, Neo4J for relationship graphs
- Real estate data aggregation from multiple sources
- Graph-based property relationship mapping (ownership, proximity, comparisons)
- REST APIs for flexible data access
- Geospatial real estate data processing
- Dedicated service instances for each data tier
- Scalable AWS infrastructure
Results & Impact
Skythena launched a real estate analytics platform leveraging purpose-built databases for different data patterns — document storage for flexible property schemas and graph queries for relationship analysis. The multi-database architecture enables complex analytics that would be difficult to achieve with a single database technology.