Scaling Revenue & Growth Tooling

This post shares how the Revenue & Growth Tools (RGT) team approaches creating full-stack tools for the teams that are the financial backbone of Netflix. Our primary partners are the teams of Revenue and Growth Engineering (RGE): Growth, Membership, Billing, Payments…

Achieving observability in async workflows

Managing and operating asynchronous workflows can be difficult without the proper tools and architecture that puts observability, debugging, and tracing at the forefront. Imagine getting paged outside normal work hours — users are having trouble with the application you’re responsible for…

My (Seemingly) Random Walk to Netflix

By Sean Barnes, Studio Production Data Science & Engineering I am going to tell you a story about a person that works for Netflix. That person grew up dreaming of working in the entertainment industry. They attended the University of Southern California, double majored in data science and television &…

Netflix SIRT releases Diffy: A Differencing Engine for Digital Forensics in the Cloud

Can you spot the difference? Hint: it’s not the bow tie. The Netflix Security Intelligence and Response Team (SIRT) announces the release of Diffy under an Apache 2.0 license. Diffy is a triage tool to help digital forensics and incident response (DFIR) teams quickly identify compromised hosts on which to…

Netflix Public Bug Bounty, 1 year later

As Netflix continues to create entertainment people love, the security team continues to keep our members, partners, and employees secure. The security research community has partnered with us to improve the security of the Netflix service for the past few years through our responsible…

Enhancing the Netflix UI Experience with HDR

Some of you have probably heard about HDR, or High Dynamic Range, which can produce a higher level of brightness, a wider range of colors, and an increased level of detail and accuracy that empowers visual storytellers to deliver more impactful experiences for…

Introducing Netflix Timed Text Authoring Lineage

By: Bhanu Srikanth, Andy Swan, Casey Wilms, Patrick Pearson The Art of Dubbing and Subtitling Dubbing and subtitling are inherently creative processes. At Netflix, we strive to make shows as joyful to watch in every language as in the original language, whether a member watches with original or dubbed audio, closed captions…

MezzFS — Mounting object storage in Netflix’s media processing platform

MezzFS (short for “Mezzanine File System”) is a tool we’ve developed at Netflix that mounts cloud objects as local files via FUSE. It’s used extensively in our media processing platform, which includes services like Archer and runs features like…

HDMI — Scaling Netflix Certification

At 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) and…

Design Principles for Mathematical Engineering in Experimentation Platform at Netflix

At Netflix, we have data scientists coming from many backgrounds such as neuroscience, statistics and biostatistics, economics, and physics; each of these backgrounds has a meaningful contribution to how experiments should be analyzed. To unlock these innovations we…

Detecting Performance Anomalies in External Firmware Deployments

Netflix has over 139M members streaming on more than half a billion devices spanning over 1,700 different types of devices from hundreds of brands. This diverse device ecosystem results in a high dimensionality feature space, often with sparse data, and can make identifying device performance issues challenging…

Netflix Studio Engineering Overview

Netflix is revolutionizing the way a modern studio operates. Our mission in Studio Engineering is to build a unified, global, and digital studio that powers the effective production of amazing content. Studio Engineering at Netflix …

Modernizing the Web Playback UI

Since 2013, the user experience of playing videos on the Netflix website has changed very little. During this period, teams at Netflix have rolled out amazing video playback features, but the visual design and user controls of the playback UI have remained the same. The visual design and user controls…

Building a Rule-Based Platform to Manage Netflix Membership SKUs at Scale

At Netflix, we aspire to entertain the world. From mailing DVDs in the US to a global streaming service with over 200 million subscribers across 190 countries, we have come a long way. For the longest time, Netflix had three plans (basic/standard/premium)…

ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

Infrastructure for Contextual Bandits and Reinforcement Learning — theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches…

Byte Down: Making Netflix’s Data Infrastructure Cost-Effective

Background on data efficiency At Netflix, we invest heavily in our data infrastructure which is composed of dozens of data platforms, hundreds of data producers and consumers, and petabytes of data. At many other organizations, an effective way to manage data infrastructure costs is to…

Evolution of Application Data Caching : From RAM to SSD

Netflix at the Spinnaker Summit 2018

Since releasing Spinnaker as an open source project in late 2015, the Spinnaker user and contributor community has grown by leaps and bounds. Our strong partnership with Google and other companies has led to a plethora of innovations and features that enable rapid, safe, and reliable delivery…

DBLog: A Generic Change-Data-Capture Framework

Overview Change-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. CDC is becoming increasingly popular for use cases that require keeping multiple heterogeneous datastores in sync (like MySQL and ElasticSearch) and addresses challenges that exist with traditional…

Engineering a Studio Quality Experience With High-Quality Audio at Netflix

Remember the epic opening sequence of Stranger Things 2? The thrill of that car chase through Pittsburgh not only introduced a whole new set of mysteries, but it returned…

Making our Android Studio Apps Reactive with UI Components & Redux

Recently Netflix has started building mobile apps to bring technology and innovation to our Studio Physical Productions, the portion of the business responsible for producing our TV shows and movies. Our very first mobile app is called Prodicle and was…

Introducing Dispatch

Netflix is pleased to announce the open-source release of our crisis management orchestration framework: Dispatch! Okay, but what is Dispatch? Put simply, Dispatch is: All of the ad-hoc things you’re doing to manage incidents today, done for you, and a bunch of other things you should’ve been doing, but have not had the time! Dispatch helps us effectively…

Computational Causal Inference at Netflix

Every 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, spanning…

Re-Architecting the Video Gatekeeper

This is the story about how the Content Setup Engineering team used Hollow, a Netflix OSS technology, to re-architect and simplify an essential component in our content pipeline — delivering a large amount of business value in the process. The Context Each movie and show on the Netflix service is carefully curated…

Netflix at Spark+AI Summit 2018

A glimpse at Spark usage for Netflix Recommendations Apache Spark has been an immensely popular big data platform for distributed computing. Netflix has been using Spark extensively for various batch and stream-processed workloads. A substantial list of use cases for Spark computation come from the various applications in the domain of content recommendations and personalization…