# Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world

DevFeed: [Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world](<https://devfeed.tech/articles/scaling-beyond-one-how-airbnb-evolved-its-data-architecture-for-a-multi-product-world-1222.md>)

Original publisher: [Read original article](<https://medium.com/airbnb-engineering/scaling-beyond-one-how-airbnb-evolved-its-data-architecture-for-a-multi-product-world-6125645d470c?source=rss----53c7c27702d5---4>)

Author: Patrick Lam

Published: 2026-06-09T17:01:02Z

Content type: article

Language: en

Sources: [The Airbnb Tech Blog - Medium](<https://devfeed.tech/sources/the-airbnb-tech-blog-medium.md>)

Topics: [data-architecture](<https://devfeed.tech/topics/data-architecture.md>), [Data Infrastructure](<https://devfeed.tech/topics/data-infrastructure.md>), [data-modeling](<https://devfeed.tech/topics/data-modeling.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [data](<https://devfeed.tech/tags/data.md>), [data-architecture](<https://devfeed.tech/tags/data-architecture.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [data-modeling](<https://devfeed.tech/tags/data-modeling.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [offline](<https://devfeed.tech/tags/offline.md>), [post](<https://devfeed.tech/tags/post.md>), [technology](<https://devfeed.tech/tags/technology.md>)

## AI overview

Airbnb's data and analytics engineering teams evolved a decade-old offline data warehouse to support Homes, Experiences, and Services. The article examines the trade-offs between separate product-specific data models and a unified monolithic model while describing the need for a consistent, flexible, and scalable data foundation.

## Source excerpt

How Airbnb's data engineers and analytics engineers built a consistent and flexible data modeling framework to support the expansion into Homes, Experiences, and Services. By: Patrick Lam, Namrata Lamba, Jamie Stober With the May 2025 Summer Release, Airbnb redesigned its app, relaunched Experiences, and debuted Services, pushing us beyond our traditional Homes focus. For the data teams, this meant rapidly evolving a decade-old infrastructure to integrate two brand-new product pillars. Our data engineers and analytics engineers rose to the challenge by building a consistent and flexible framework to serve as a robust and scalable data foundation for the next decade of growth. But getting there wasn't straightforward. This fundamental shift surfaced a critical question for our data organization: How do you evolve your offline data architecture to support new product lines without introducing disorder in vital analytics services? We knew the approach we took would have long-lasting implications. A fragmented strategy risked creating data silos, inconsistent analytics, and a tangled web of technical debt that would likely slow down future innovation. In this post, we'll take you behind the scenes to share key decisions that we made, the framework that emerged, and the lessons that helped reshape our offline data warehouse for the future. Note that we focus specifically on our offline data warehouse (the analytics-oriented data infrastructure owned by our data engineers and analytics engineers) rather than the online data systems that serve the app directly, as the two domains have fundamentally different requirements, constraints, and design philosophies that warrant separate treatment. The core dilemma: separate vs. monolithic The first and most critical question was how to structure offline data for the new, three-product world, with Homes, a refreshed Experiences product, and the new Services offering. This involved a trade-off between two main approaches: Separate