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Evolution and Scaling of Feature Store at Uber

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Palette runs 21,000+ features at 25M QPS with 99.999% uptime and shows exactly how that scale is achieved: ingest via Kafka, storage in Hive (offline) and Cassandra (online), and processing with Spark plus Flink for streaming. Users onboard features by submitting a JSON config; the platform applies auto-config, batching, incremental materialization and resume checkpoints to avoid full recompute. Delta and selective dispersals plus local/Redis/Cassandra caching and a metadata tiering system handle cost, freshness and online/offline consistency; embedding support (Sia) is in progress.

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