Browse past weeks of engineering reads.
Reducing LLM evaluation iteration cycles from weeks to a day to enable fast experimentation on non-deterministic model improvements in production systems.
Airbnb needed to evolve its decade-old data architecture to support three distinct product pillars (Homes, Experiences, Services) with consistent data modeling and flexible frameworks.
Reliably delivering configuration changes to thousands of Airbnb service instances in Kubernetes, with changes occurring multiple times per minute at scale.
Building reliable forecasting models for marketplace demand when historical data is unavailable or unreliable due to unprecedented market shocks.
Airbnb needed to transition Viaduct from an internal-only data mesh tool to a production-ready, community-driven platform with a stable public API.
Designing monitoring and observability systems that remain functional and reliable even when the core infrastructure they monitor is failing or degraded.
How to build a durable workflow execution engine that can recover from failures mid-process without losing state or duplicating work.
Building a metrics storage system capable of ingesting 50 million samples per second while reliably storing 2.5 petabytes of time series data at scale.
Migrating a large-scale metrics pipeline from StatsD to OpenTelemetry while handling production traffic volumes without losing data or blocking dependent systems.
Building forecasting models that remain accurate during sudden market shocks like a global pandemic, where historical data no longer predicts future outcomes.
Airbnb's reliance on multiple third-party observability vendors resulted in inconsistent data, fragmented developer experiences, and limitations in cost-effectiveness and reliability at their scale.
Airbnb's Observability as Code alert development process had excessively long development cycles (weeks) due to cumbersome code review workflows, slowing down engineers' ability to create and iterate on alerts at scale across thousands of services.