The lakehouse architecture has become a foundational design for modern data and AI workloads. But this flexibility comes at a cost: users and system developers must navigate multiple APIs, conflicting abstractions, and overlapping execution models. What if we started from scratch? In this talk, we discuss the technical challenges of building a "Function-as-a-Service" (FaaS) lakehouse. We argue that existing FaaS platforms were never designed for data-intensive workflows.
To address this, we built a new system from the ground up using object storage and open formats. Re-purposing lessons from OpenLambda, we deploy functions up to 15× faster than AWS Lambda. By extending Apache Iceberg’s isolation with Git-like primitives, we support multi-language transactions with formal correctness proofs. We conclude by emphasizing the role of user-facing APIs for adoption in real-world settings, and sharing late-breaking results from our ongoing research.
00:00 - Intro