# Engineering Features at Scale: Inside the Darwin Feature Store

DevFeed: [Engineering Features at Scale: Inside the Darwin Feature Store](<https://devfeed.tech/articles/engineering-features-at-scale-inside-the-darwin-feature-store-22619.md>)

Original publisher: [Read original article](<https://medium.com/dreamlockerroom/engineering-features-at-scale-inside-the-darwin-feature-store-ed5928752e8a?source=rss----5c7a7f580b01---4>)

Author: Dream Blog

Published: 2026-01-27T15:38:25Z

Content type: article

Language: en

Sources: [Dream11 Engineering](<https://devfeed.tech/sources/dream11-engineering.md>)

Topics: [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Apache Cassandra](<https://devfeed.tech/topics/cassandra.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [cassandra](<https://devfeed.tech/tags/cassandra.md>), [data](<https://devfeed.tech/tags/data.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [feature-store](<https://devfeed.tech/tags/feature-store.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [latency](<https://devfeed.tech/tags/latency.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [offline](<https://devfeed.tech/tags/offline.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [tech](<https://devfeed.tech/tags/tech.md>)

## AI overview

Dream Horizon's Darwin Feature Store is an open-source, low-latency feature platform designed to support real-time machine learning and offline training at Dream11. The article describes the challenges that led to its development, including batch-only pipelines, manual schema changes, and scaling issues. It reports serving more than 200 million feature requests per minute with p99 read latency below 5 milliseconds and no production incidents over the preceding year.

## Source excerpt

By Mohit Jain and Ujjwal Bagrania Dream Horizon, our open-source effort to make Dream11's battle-tested tech available to every developer, brings you the Darwin Feature Store -- a unified, low-latency feature platform built to power real-time ML at scale, and shaped to help teams build, manage, and trust features in production. Explore the Darwin Feature Store here. At Dream11, data and ML have always powered how millions of users experience sports in real time. From personalisation and relevance to ensuring efficiency under peak match traffic, ML sits deep in the critical path of the product. But models are only half the story. The real challenge is managing features -- how they're defined, versioned, and accessed consistently across training and live traffic, especially under peak sports pressure. And at the scale we operate at, feature access doesn't mean the occasional lookup; it means hundreds of millions of requests per minute, spanning both real-time inference and offline training pipelines. As Dream11 grew, feature engineering stopped being something we could manage with pipelines and tables, and became infrastructure that everything else depended on. That shift forced us to build the Darwin Feature Store: a system designed not just to tackle sports-scale traffic, but to make feature engineering predictable, reliable, and developer-friendly in production. Today, that translates into serving 200M+ feature requests per minute, delivering p99 read latencies under 5 ms, and doing so reliably through the most demanding live sports moments, without a single production incident over the last year. In this post, we'll walk through how we built the Feature Store, and what it takes to serve features reliably. The Early Days: Batch-Only, Fragile, and Manual Before the Darwin Feature Store existed, our feature engineering was mostly a few pipelines, a lot of Cassandra tables, and an increasing number of engineers building ML models. Here's what the first version looked li