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Why should you consider ScyllaDB as a feature store?¶
ScyllaDB is a real-time NoSQL database that is best suited for feature store use cases where you require low latency (e.g. model serving), high throughout (e.g. training) and need peta-byte scalability.
Feature store is a central data store to power operational machine learning models. They help you store transformed feature values in a scalable and performant database. Real-time inference requires features to be returned to applications with low latency at scale. This is where ScyllaDB can play a crucial role in your machine learning infrastructure.
What ScyllaDB brings to the table:
Low-latency: ScyllaDB can provide <1 ms P99 latency. For real-time machine learning apps, an online feature store is required to meet strict latency requirements. ScyllaDB is an excellent choice for an online store (Read how Medium is using ScyllaDB as a feature store.)
High-throughput: Training requires querying huge amounts of data and processing large datasets with possibly millions of operations per second - something that ScyllaDB excels at.
Large-scale: ScyllaDB can handle petabytes of data while still keeping latency low and predictable.
High availability: ScyllaDB is a highly available database. With its distributed architecture, ScyllaDB keeps your feature store database always up and running.
Easy to migration: ScyllaDB is compatible with DynamoDB API and Cassandra which means it’s simple to migrate over from legacy solutions.
Integration with Feast: ScyllaDB integrates well with the popular open-source feature store framework, Feast. Example architecture with Feast and ScyllaDB:
