In this episode of The Data Engineering Show, host Benjamin sits down with Nikhil Simha, CTO of Zipline AI and co-author of Chronon, to explore how a declarative feature platform solves the speed-vs-scale paradox in modern ML infrastructure, from fraud detection at Airbnb to powering OpenAI's recommendation systems.

Nikhil Simha is the CTO at Zipline AI, bringing extensive experience from leadership roles at Airbnb and Facebook. He is a co-author of Chronon, an open-source feature engineering platform that automates the generation of ML infrastructure from declarative queries. With deep expertise in real-time data systems, fraud detection, and feature engineering at scale, Nikhil has architected solutions powering recommendation systems and risk detection across billions of user interactions. In this episode, he shares insights on building scalable ML infrastructure, integrating LLMs with real-time feature contexts, and the evolving data engineering landscape. His work has directly impacted how organizations from early-stage startups to Fortune 500 companies approach feature engineering and real-time ML serving, making this conversation essential for engineers building production AI systems.

"Fraud is adversarial. Right? Like, someone comes up with a new way to do fraud somewhere around the world, and people at Airbnb need to react to it very quickly." - Nikhil

"Chronon, at its core, generates these systems from queries. So users write queries on Chronon, and we generate all of these under the hood." - Nikhil

"Chronon allows data scientists to operate independently." - Nikhil

"The main problem there was that the traditional model of data scientists writing some logic and ML engineers going and billing system out for that logic, that was too slow for fraud detection." - Nikhil

"They have to come up with a new model in a matter of days. They don't have, like, this three to five month period where they can sit and create the new model, build all of these pipelines." - Nikhil

"There is a real gap in the industry for an engine that goes all the way from single machine scale to thousands of machine scale seamlessly." - Nikhil

"Most people, for ninety-five percent of their queries, don't need Spark in RPA. Right? But there is that 5% usually, like, a lot of ML falls into that." - Nikhil

"We are handling query fragments. Right? We take query fragments, generate very specialized logic for that, and run that through Spark's distributed processing topologies." - Nikhil

"The new trend in the industry would be, like, towards these engines that can work at any scale and be useful for interactive and large processing workloads." - Nikhil

"I think Iceberg is great that way because you're not fragmenting to different proprietary data formats, different proprietary engines." - Nikhil

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