Feature stores and platforms — the DoorDash arc, Airbnb's Chronon, Uber's Michelangelo — plus training-data pipelines and the AI-era stack.
MLOps is mostly data engineering. The proof is the feature-platform canon: DoorDash's Riviera→Fabricator→Workbench arc, Airbnb's Chronon (run at Stripe too), LinkedIn's Feathr, Uber's three Michelangelo generations, and the embedding-era stack — where interviews are heading as DE and ML infra converge.
The serving stack, budgeted: layout beats engine choice, a client cache absorbs the repetition, cold tails route to disk by cardinality × read rate, and streaming state stays bounded by design.
The feature store bill doubled two quarters running, and the capacity review is grim: the Redis fleet crossed one hundred nodes, every resize is a two-day blue-green ceremony, and feature count grew 10× in a year with no ceiling in sight. Meanwhile the serving graphs show something odd: tens of millions of reads per second, but the *same* entities — the same restaurants, the same busy users — fetched over and over, milliseconds apart, at full price each time. Storage priced by memory, reads priced by volume, and a workload that's mostly repetition. Three different inefficiencies are hiding in that one bill. Can you name them?
The full week 2 brief is part of LeetData Pro.
