How Agoda Scaled Its Feature Store 50X with ScyllaDB

2026-06-04 · Source: HackerNoon · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Agoda significantly scaled its feature store by 50 times through the implementation of ScyllaDB, a high-performance NoSQL database solution. This case study, published on June 4th, 2026, by ScyllaDB, details how the travel platform achieved substantial improvements in its data infrastructure. The scaling initiative focused on optimizing the feature store architecture, likely utilizing ScyllaDB's NVMe-optimized storage and advanced compaction strategies to ensure low-latency database operations. The project also addressed critical concerns such as cache stampede prevention and integrated with caching technologies like DragonflyDB, demonstrating a comprehensive approach to managing and serving machine learning features at an enterprise scale.

Key takeaway

For MLOps Engineers or AI Architects designing scalable feature stores, Agoda's 50x scaling with ScyllaDB highlights a viable path for extreme performance. You should evaluate ScyllaDB's NoSQL capabilities, especially its NVMe optimization and compaction strategies, to achieve low-latency data serving. Consider integrating robust caching solutions like DragonflyDB to prevent cache stampedes and ensure your machine learning models receive features efficiently.

Key insights

Agoda scaled its feature store 50x using ScyllaDB for high-performance, low-latency data management.

Principles

• High-performance NoSQL databases enable extreme scaling.
• Feature store architecture benefits from NVMe optimization.
• Effective caching prevents cache stampedes.

Method

The article implies a method involving migrating or integrating a feature store with ScyllaDB, optimizing for NVMe, and implementing compaction strategies alongside caching solutions like DragonflyDB.

In practice

• Evaluate ScyllaDB for high-throughput feature stores.
• Consider NVMe drives for database performance.
• Implement caching with DragonflyDB for latency.

Topics

• Feature Store
• ScyllaDB
• NoSQL Database
• Low-Latency Scaling
• NVMe Optimization
• Cache Management

Best for: Machine Learning Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by HackerNoon.