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We just published a new ScyllaDB sample application, a video streaming app. The project is available on GitHub. This blog covers the video streaming application’s features and tech stack and breaks down the data modeling process.
The app has a minimal design with the most essential video streaming application features:
Machine learning (ML) feature stores have been attracting attention and usage for business-critical applications ever since Uber introduced the concept with Michelangelo in 2017. In this blog post, we will delve into the fundamentals of ML feature stores and explore why and how ScyllaDB can be a critical part of your feature store architecture.
In order to understand what feature stores are, it’s important first to understand what features are.
This tutorial will show you how to create an IoT app from scratch using Rust and configure it to use ScyllaDB as the backend NoSQL datastore. It’ll walk you through all the stages of the development process, from gathering requirements to building and running the application.
As an example, you will use an application called CarePet. CarePet allows pet owners to track their pets’ health by monitoring their key health parameters, such as temperature or pulse. The application consists of three parts: