AI/ML Distributed Database Use Cases

How ShareChat Scaled their ML Feature Store 1000X without Scaling the Database

How ShareChat successfully scaled 1000X without scaling the underlying database (ScyllaDB)

Using ScyllaDB to Implement Lists in Medium’s Feature Store

Discover how Medium is leveraging ScyllaDB to power a fast, scalable data layer for lists in Medium’s feature store.

Feature Store Evolution Under Cost Constraints: When Cost is Part of the Architecture

Inside Tripadvisor’s Real-Time Personalization with ScyllaDB + AWS

See the engineering behind real-time personalization at Tripadvisor’s massive (and rapidly growing) scale

MongoDB vs ScyllaDB: Tractian’s Experience with Real-Time ML

Get practical insights into Tractian’s MongoDB to ScyllaDB migration process, including challenges, lessons learned, and the impact on product performance.

What is an ML Feature Store and How Can ScyllaDB Help You Build One?

Delve into the fundamentals of ML feature stores and explore why and how ScyllaDB can be a critical part of your feature store architecture.

Building Low Latency ML Systems for Real-Time Model Predictions at Xandr

Learn about the challenges of building an ML system with the low latency required to support the high volume and high throughput demands of ad serving.

ScyllaDB vs MongoDB vs PostgreSQL: Tractian’s Benchmarking & Migration

TRACTIAN's comparison of ScyllaDB vs MongoDB and PostgreSQL, plus their migration challenges & results.