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How Our Paths Brought Us to Data and Netflix

Part of our series on who works in Analytics at Netflix — and what the role entailsby Julie Beckley & Chris PhamThis Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.Photo from a team curling offsite — There’s...

Analytics at Netflix: Who we are and what we do

Analytics at Netflix: Who We Are and What We DoAn Introduction to Analytics and Visualization Engineering at Netflixby Molly Jackman & Meghana ReddyExplained: Season 1 (Photo Credit: Netflix)Across nearly every industry, there is recognition that data analytics is key to driving informed business decision-making. But there is far less agreement...

Seamlessly Swapping the API backend of the Netflix Android app

How we migrated our Android endpoints out of a monolith into a new microserviceby Rohan Dhruva, Ed BallotAs Android developers, we usually have the luxury of treating our backends as magic boxes running in the cloud, faithfully returning us JSON. At Netflix, we have adopted the Backend for Frontend (BFF) pattern: instead of having one general purpose...

HDMI — Scaling Netflix Certification

HDMI — Scaling Netflix CertificationScott Bolter, Matthew Lehman, Akshay Garg ¹At Netflix, we take the task of preserving the creative vision of our content all the way to a subscriber TV screen very seriously. This significantly increases the scope of our application integration and certification processes for streaming devices like set-top-boxes (STBs)...

Edgar: Solving Mysteries Faster with Observability

Edgar helps Netflix teams troubleshoot distributed systems efficiently with the help of a summarized presentation of request tracing, logs, analysis, and metadata.by Elizabeth CarrettoOur Team — Kevin Lew, Maulik Pandey, Narayanan Arunachalam, Dustin Haffner, Andrei Ushakov, Seth Katz, Greg Burrell, Ram Vaithilingam, Mike Smith and Elizabeth CarrettoEveryone...

Key Challenges with Quasi Experiments at Netflix

Kamer Toker-Yildiz, Colin McFarland, Julia GlickAt Netflix, when we can’t run A/B experiments we run quasi experiments! We run quasi experiments with various objectives such as non-member experiments focusing on acquisition, member experiments focusing on member engagement, or video streaming experiments focusing on content delivery. Consolidating on...

Optimized shot-based encodes for 4K: Now streaming!

by Aditya Mavlankar, Liwei Guo, Anush Moorthy and Anne AaronNetflix has an ever-expanding collection of titles which customers can enjoy in 4K resolution with a suitable device and subscription plan. Netflix creates premium bitstreams for those titles in addition to the catalog-wide 8-bit stream profiles¹. Premium features comprise a title-dependent...

Telltale: Netflix Application Monitoring Simplified

By Andrei Ushakov, Seth Katz, Janak Ramachandran, Jeff Butsch, Peter Lau, Ram Vaithilingam, and Greg BurrellOur Telltale VisionAn alert fires and you get paged in the middle of the night. A metric crossed a threshold. You’re half awake and wondering, “Is there really a problem or is this just an alert that needs tuning? When was the last time somebody...

Computational Causal Inference at Netflix

Jeffrey Wong, Colin McFarlandEvery Netflix data scientist, whether their background is from biology, psychology, physics, economics, math, statistics, or biostatistics, has made meaningful contributions to the way Netflix analyzes causal effects. Scientists from these fields have made many advancements in causal effects research in the past few decades,...

Improving our video encodes for legacy devices

by Mariana Afonso, Anush Moorthy, Liwei Guo, Lishan Zhu, Anne AaronNetflix has been one of the pioneers of streaming video-on-demand content — we announced our intention to stream video over 13 years ago, in January 2007 — and have only increased both our device and content reach since then. Given the global nature of the service and Netflix’s commitment...

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