AI/ML Distributed Database Use Cases

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

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

Using ScyllaDB for Medium's Feature Store

Learn how Medium is using ScyllaDB to build a fast and scalable data layer for lists in their feature store.

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.

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

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

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

The Harsh Reality of Building a Real-Time ML Feature Platform

Efficient Deduplication in RecSys at Scale: How We Managed to Reduce Server Cost by 90%

Efficient deduplication is key to delivering relevant recommendations at scale. Andrei shares how they optimized for 300M+ users, cutting costs by 90% while reducing latency. He covers how ScyllaDB, A

Tutorial: Build a Machine Learning Feature Store

This tutorial walks you through a sample feature store application with ScyllaDB Cloud.

Real-Time Machine Learning with ScyllaDB as a Feature Store

What ML feature stores require and how ScyllaDB fits in as fast, scalable online feature store.

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.

How Agoda Scaled 50x Throughput with ScyllaDB

Migrating 50TB Data From a Home-Grown Database to ScyllaDB, Fast

Terence share how Clearview AI's infra needs evolved and why they chose ScyllaDB after first-principles research. From fast ingestion to production queries, the talk explores their journey with Rust,

Vector Search with ScyllaDB

How ScyllaDB Helps an AI-Powered AdTech Company Focus on Core Business

AdTech innovator GumGum wanted to escape the maintenance issues connected with Apache Cassandra. This podcast shares how they moved to a database-as-a-service (ScyllaDB Cloud DBaaS).

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.

Why TRACTIAN Migrated from MongoDB to ScyllaDB for Real-Time ML

TRACTIAN’s ML model workloads increased over 2X in a year. Here’s why they changed databases and their lessons learned