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Leave the Class Path in the Rearview Mirror
Introducing composable, module system native and agent friendly command line tools for modern Java developmentBy Danny Thomas, JVM Ecosystem TeamRecent work on the Java language to pave the on-ramp has...
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MAPS: Netflix’s Multimodal Asset Personalization at Scale
By Emma Yanyang Kong, Aditya Deshpande, Asad Abbasi, Bowei Yan, David Fagnan, Ashish Rastogi, Dhaval Patel, Ray ZhangIntroductionThe Netflix experience is a journey of discovery. Every visual cue, from...
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A Tale of Two Flink Autoscalers
Samuel Yeboah, Francesco Di Chiara and Mingliang LiuToday, Netflix runs two Flink autoscalers. That is exactly one more than we want. We built the first one in-house years ago, when there was no mature...
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How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying...
How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying the graph with gRPC execution APIAuthors: Nilesh Mishra and Ajit KotiThis is the third entry of a multi-part blog series...
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Modeling Device Capabilities for Analytics
by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh SelverajNetflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live...
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GenRec: Towards LLM-Native Recommendation at Netflix
Authors: Ying Li, Arjun Rao, Shradha SehgalIntroductionRecommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users,...
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In-House LLM Serving at Netflix
By AI Platform’s Model Runtime team and Inference teamIntroductionMost organizations consume LLMs through hosted APIs. Netflix went further — we run the full stack ourselves, from model deployment...
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Building Service Topology at Scale: Architecture, Challenges, and Lessons...
By Parth Jain, Rakesh Sukumar, Yingwu Zhao, Renzo Sanchez-Silva & Nathan FisherA deep dive into the engineering challenges of building a real-time service dependency map at Netflix scale: from...
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GenPage: Towards End-to-End Generative Homepage Construction at Netflix
Authors: Lequn Wang, Jiangwei Pan, and Linas BaltrunasFigure 1. Autoregressive homepage generation. GenPage builds a Netflix homepage one row or entity at a time, each one conditioned on what’s already...
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Toward More Controllable AI Video Editing: An Early Research Exploration at...
By Zhuoning Yuan, Ta-Ying Cheng, Benjamin Klein, Bahareh AzarnoushIntroductionAt Netflix, we build technology to help storytellers bring their creative visions to life and to help members discover the...
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How Netflix Simplified Batch Compute with Kueue
By Alvin Bao, Alex Petrov, Jennifer Lai, Aidan Sherr, and Samartha ChandrashekarAs a part of the journey to transition Netflix’s compute infrastructure to be more Kubernetes-native, we have leaned into...
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The Data Canary: How Netflix Validates Catalog Metadata
By Celina AmadosAt Netflix, our catalog metadata is crucial to our member experience, and a single corrupted data state can impact millions of viewers immediately. To protect streaming reliability, we...
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Data Projects: Managing Data Assets at Netflix Scale
By Amer Hesson, Marcelo Mayworm, James Mulcahy, and Brittany TruongThe Problem: Managing Assets at Netflix ScaleNetflix’s Data Platform is vast. We have millions of tables in our data warehouse and...
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Predicting Risk in Content Launches: How Data-Driven Insights can Transform...
by Emily GillEach year, we bring the Analytics Engineering community together for an Analytics Summit — a multi-day internal conference to share analytical deliverables across Netflix, discuss analytic...
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The Evolution of Cassandra Data Movement at Netflix
By Guil Pires, Jennifer Prince, Jose Camacho, Ken Kurzweil, Phanindra ChunduruBackgroundIn a previous post, we introduced Data Bridge, a unified management plane for batch Data Movement at Netflix....
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Thinking Fast & Slow for a Personalized Notification System
by Matthew Wood, Ishan Gupta, Kevin Mercurio, Devon Bryant, and Claire DormanIn his seminal book “Thinking, Fast and Slow,” Daniel Kahneman describes two systems that drive human cognition: System 1,...
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A Human-Augmenting Agentic Workflow for Causal Inference
By Winston Chou, Adrien Alexandre, Lars Olds, Yi Zhang, Garrett Hagemann, and Nathan KallusIntroductionImagine asking a data agent to analyze the causal relationship between two variables, such as the...
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VMAF v1: Good Is Not Good Enough
By Christos G. Bampis, Zhi Li, Kyle Swanson, Nil Fons Miret and Pavan MadhusudanaraoWill this encode look good to Netflix members? Does switching to a new codec improve quality at the same bitrate and...
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Dynamic Repartitioning for Time Series Workloads
By Rajiv Shringi, Kaidan Fullerton, Oleksii Tkachuk and Kartik SathyanarayananIntroductionNetflix’s TimeSeries Abstraction is a scalable system for ingesting and querying petabytes of temporal event...
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High-Throughput Graph Abstraction at Netflix: Part I
By Oleksii Tkachuk, Kartik Sathyanarayanan, Rajiv ShringiIntroductionNetflix has a diverse range of graph use cases, each serving specific business needs with unique functionality and performance...
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