The Good Tech Companies - ScyllaDB Powers Low-Latency, Scalable Online Feature Stores for Real-Time ML

Episode Date: September 18, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/scylladb-powers-low-latency-scalable-online-feature-stores-for-real-time-ml. Discover how ScyllaDB enables fast, scalable online feature stores, integrating with Feast to deliver low-latency, high-throughput ML predictions. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #scylladb-feature-store, #online-feature-store, #real-time-ml-inference, #low-latency-database, #feast-integration, #machine-learning-features, #high-throughput-nosql, #good-company, and more. This story was written by: @scylladb. Learn more about this writer by checking @scylladb's about page, and for more stories, please visit hackernoon.com. Feature stores centralize ML features for training and inference, but real-time workloads demand ultra-low latency. ScyllaDB’s shard-per-core design and Cassandra/DynamoDB compatibility make it an ideal online store, supporting single-digit ms queries and petabyte-scale throughput. With Feast integration, teams can avoid vendor lock-in and build scalable, real-time ML apps.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Ciladiby powers low latency, scalable online feature stores for real-time ML. By Skyla DB, what ML feature stores require and how Ciladib fits in as fast, scalable online feature store in this blog post. We'll explore the role of feature stores in real-time machine learning, ML, applications, and why Cilidibi is a strong choice for online feature serving. We'll cover the basics of features. how feature stores work, their benefits, the different workload requirements,

and how latency plays a critical role in ML applications. We'll wrap up by looking at popular feature store frameworks like Feast and how to get started with CILADIB as your online feature store. What is a feature in machine learning? A feature is a measurable property used to train or serve a machine learning model. Features can be raw data points or engineered values derived from the raw data. For instance, in a social media app like ShareChat, features might include number of likes in the last 10 minutes. Number of shares over the past seven days. Topic of the post, Image Credit, Ivan Bermistrov and Andrea Manikov, share chat.

These data points help predict outcomes such as user engagement or content recommendation. A feature vector is simply a collection of features related to a specific prediction task. For example, this is what a feature vector could look like for a credit scoring application. Selecting relevant data points and transforming them into features takes up a significant portion of the work in machine learning projects. It is also an ongoing process to refine and optimize features so the model being trained becomes more accurate over time. Feature store architectures. In order to efficiently work with features, you can create a central place tow manage the features that are available within your organization. A central feature store enables a standard process to create new features. Storage of features for simplified access, discovery and reuse of features across teams,

serving features for both model training and inference. Most architectures distinguish between two stores, databases, offline store for model training, bulk writes, reads, online store for inference, real-time, low latency rights, reads. A typical feature store pipeline starts with ingesting raw data from data lakes or streams, performing feature engineering, saving features in both. stores and then serving them through two separate pipelines, one for training and one for inference. Benefits of a centralized feature store. Centralized feature stores offer several advantages, avoid duplication. Teams can reuse existing features. Self-serve access. Data scientists

can generate and query features independently. Unified pipelines. Even though training and inference workloads are vastly different, they can still be queried using the same abstraction layer. This results in faster iteration, more consistency, and better collaboration across ML workflows. Different workloads in feature stores. Let's break down the two very distinct workload requirements that exist within a feature store, model training in real-time inference. 1. Model Training, offline store. In order to make predictions you need to train a machine learning model first. Training requires a large and high-quality dataset. You can store this dataset in an offline feature store. Here's a rundown of what characteristics matter most for model training workloads.

Latency. Not a priority. Volume. High. Millions to billions of records. Frequency. In frequent. Schedule jobs. Purpose. Retrieve a large chunk of historical data. Basically, offline stores need to efficiently store huge datasets. Two. Real time inference, online store. Once you have a model ready, you can run real time inference. Real time inference. takes the input provided by the user and turns it into a prediction. Here's a look at what characteristics matter most for real-time inference. Latency. High priority. Volume.

Low per request but high throughput. Up to millions of operations per second. Frequency. Constant triggered by user actions. E.G. Ordering food. Purpose. Serve up-to-date features for making predictions quickly. For example, consider a food delivery app.

The user's recent cart contents, age, and location might be turned into features and used instantly to recommend other items to purchase. This would require real-time inference, and latency makes or breaks the user experience. Why latency matters, latency, in the context of this article, refers to the time between sending a query and receiving the response from the feature store. For real-time ML applications, especially user-facing one's low latency is critical for success. Imagine a user at checkout being shown related food items. If this suggestion takes too long to load due to a slow online store, the opportunity is lost. Feint to end flow from ingesting the latest data, querying relevant features, running inference, returning a prediction

must happen in milliseconds, choosing a feature store solution. Once you decide to build a feature store, you'll quickly find that there are redosons of frameworks and providers, both open source and commercial to choose from feast open source provides flexible database support e g postgres redis cassandra ciladibi hopsworks tightly coupled with its own ecosystem oz sage maker tied to the a ws stack e g s3 dynamo db and lots of others which one is best factors like your team's technical expertise latency requirements and required integrations with your existing stack all play a role. There's no one-size-fits-all solution. If you are worried about the scalability and performance of your online feature store, then database flexibility should be a key consideration. There are feature stores, E, G, AWS SageMaker,

GCP Vertex, hopsworks, etc. That provideth their own database technology as the online store. On one hand, this might be convenient to get started because everything is handled by one provider. But this can also become a problem later on. Imagine choosing a vendor like this with a strict P-99 latency requirement, E-G, less than 15 milliseconds P-99. The requirement is successfully met during the proof of concept, POC. But later you experience latency spikes, maybe because your requirements change or there's a surge of new users in your app or some other unpredictable reason. You want to switch to a different online store database back-end to save costs. The problem is you cannot, at least not easily.

You are stuck with a built-in solution. It's unfeasible to migrate off just the online store part of your architecture because everything is locked in. If you want to avoid these situations, you can look into tools that are flexible regarding the offline and online store backend. Tools like Feast or feature Formalow you to bring your own database backend, both for the online and offline stores. This is a great way to avoid vendor lock in and make future database emigrations less painful

in case latency spikes occur or costs rise. Cilidibi is an online feature store. Ciladibi is a high-performance in OSQL database that's API compatible with Apache Cassandra and DynamoDB API. It's implemented in C++ uses a shard per core architecture and includes an embedded cache system, making it ideal for low latency, high throughput feature store applications. Why Ciladibi, low latency, single-digit millisecond P99 performance, high availability, availability and resilience, high throughput at scale, petabyte scale deployments, no vendor lock-in, runs on-prem or in any cloud. Drop-in replacement for existing Cassandra, DynamoDB setups. Easy migration from

other NoSQL databases, Cassandra, DynamoDB, MongoDB, etc. Integration with the feature store framework feast. CILADB shines in online feature store use cases where real-time performance, availability, and and latency predictability are critical. CILADIB plus Feast integration. Feast is a popular open source feature store framework that supports both online and offline stores. One of its strengths is the ability to plug in your OWN database sources, including CILADIB. Read more about the CILADB plus Feast integration in the docs. Get started with a feature store tutorial. Want to try using CILADB as your online feature store?

Check out our tutorials that walk you through the process of creating a CILADB cluster and and building aerial time inference application. Tutorial. Price prediction inference app with CILADIB tutorial. Realtime app with Feast and CILADB Feast plus CILADB integration GitHub. CilidiB as a feature store code examples have questions or want help setting it up.

Submit a post in the forum, update. I just completed a developer workshop with Feast Maintainer, Francisco Javier Arceo. Build real time ML apps with Python, Feast and NoSQL. You can watch it on demand now. Thank you for listening to this Hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn and publish.