Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale By Andrew Pierce, Chris Thrailkill, Victor Chiapaikeo At Netflix, we prioritize getting timely data and insights …

The Netflix Tech Blog provides insights into the engineering practices, architecture, and innovations behind the Netflix streaming platform. Developers can learn about Netflix's microservices architecture, data infrastructure, and machine learning algorithms, as well as explore topics such as video encoding, content delivery, and personalization algorithms used in the Netflix service.

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Netflix evolved their Muse analytics platform to handle trillion-row datasets by implementing HyperLogLog sketches for approximate distinct counts, using Hollow for in-memory precomputed aggregates, and extensively tuning their Apache Druid cluster. The migration reduced query latencies by 50% while supporting advanced filtering capabilities for creative content insights. The team used parallel stack deployment, automated validation, and granular feature flags to ensure data accuracy during the transition.

Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale

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