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Implementation of In-Memory Data Grid for Scalable Hotel Availability Search System

stratoflow.com

Hospitality & leisure

Information technology

eCommerce

Scalability and Cost Inefficiency in Hotel Booking System

Existing SQL Server cluster architecture cannot horizontally scale to handle 200M+ daily read queries. Vertical scaling limitations and diminishing returns (0.1% throughput increase per 2% infrastructure cost) prevent business growth. High latency impacts customer query frequency and revenue potential.

About the Client

Digital platform aggregating hotel inventory data from multiple providers for B2B and B2C bookings

System Modernization Goals

Reduce persistent database load by 80%
Achieve linear horizontal scalability for search operations
Decrease average response time by 60%
Support 50%+ traffic growth without proportional cost increases
Enable real-time data updates while maintaining performance

Core System Requirements

IMDG-based availability search engine
High-speed data ingestion pipeline from SQL Server
Incremental update mechanism via message queue
Low-latency query API for booking systems
Automated horizontal scaling configuration

Technology Stack

In-Memory Data Grid (e.g., Hazelcast, Redis)

Message Queue (e.g., Kafka, RabbitMQ)

SQL Server integration layer

Distributed caching architecture

System Integrations

Existing SQL Server database cluster
Hotel inventory management systems
Third-party booking platforms
Monitoring and alerting infrastructure

Operational Requirements

99.99% availability SLA
Linear scalability up to 1B queries/day
Sub-100ms latency for 95% of queries
Data consistency across distributed nodes
Automated failover mechanisms

Business Impact Projections

Infrastructure cost reduction of 80% with 50%+ increased query capacity. Response time improvements expected to drive 20-30% revenue growth from existing clients through increased query frequency. Enables expansion to new markets with high-volume demand potential.