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Modernized Cloud-Based Data Architecture for Real Estate Analytics Platform
  1. case
  2. Modernized Cloud-Based Data Architecture for Real Estate Analytics Platform

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Modernized Cloud-Based Data Architecture for Real Estate Analytics Platform

leobit.com
Real estate
Information technology
Data management

Legacy Data Architecture Limitations

The client's existing data infrastructure relied on Azure SQL with in-memory computations and monolithic stored procedures, causing severe performance bottlenecks. Processing 270,000+ records took over 15 minutes, with escalating delays due to hourly data updates. The system lacked scalability, required full rebuilds for minor changes, and could not support dynamic updates or cloud-native efficiencies.

About the Client

A real estate platform managing large volumes of property, pricing, tax, and geographic data across the U.S., requiring scalable analytics capabilities.

Modernization Goals

  • Replace legacy in-memory processing with cloud-native BigQuery architecture
  • Reduce data processing time by 800% or more
  • Enable scalable, node-distributed data operations
  • Implement dynamic update mechanisms for incremental data changes
  • Ensure seamless integration with existing Google Sheets analytics workflows

Core System Capabilities

  • Azure SQL to BigQuery data synchronization pipelines
  • Optimized stored procedures for distributed cloud processing
  • BigQuery views with embedded aggregation logic
  • Incremental data append/update functionality
  • Google Sheets integration for reporting

Technology Stack

GCP BigQuery
Azure SQL
.NET
AppScript

System Integrations

  • Azure SQL database connectors
  • BigQuery-Google Sheets API integration
  • RESTful WebAPI endpoints

Operational Requirements

  • Horizontal scalability for petabyte-level data growth
  • Sub-minute incremental update processing
  • 99.99% system availability SLA
  • Role-based access control (RBAC) security model
  • Automated failover and disaster recovery

Business Value Realization

The modernized architecture enables real-time analytics on exponentially growing datasets, reducing full rebuild times from hours to minutes. Dynamic update capabilities eliminate system-wide disruptions for minor data changes, while cloud-native scalability ensures long-term adaptability. Performance improvements directly translate to faster business decisions and 80%+ reduction in infrastructure costs compared to legacy systems.

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