Netflixにおける日本語字幕の導入

Netflixでは、2015年9月の日本における配信サービス開始時から日本語字幕を提供して …

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…

Netflix Cloud Security: Detecting Credential Compromise in AWS

Credential compromise is an important concern for anyone operating in the cloud. The problem becomes more obvious over time, as organizations continue to adopt cloud resources as part of their infrastructure without maintaining an accompanying capability to observe and react to these compromises…

Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused…

Today we’re excited to announce that we’re open sourcing Mantis, a platform that helps Netflix engineers better understand the behavior of their applications to ensure the highest quality experience for our members. We…

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…

Announcing the Netflix Post Technology Alliance

You’re working on the next great show or film that will deliver to Netflix. You know Netflix has a high quality bar for its content and you want the delivery to go smoothly. Do you know if the tools you have invested in can…

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 …

Android Rx onError Guidelines

“Creating a good API is hard.” — anyone who has created an API used by others As with any API, wrapping your data stream in a Rx observable requires consideration for reasonable error handling and intuitive behavior. The following guidelines are intended to help developers create consistent…

Open-sourcing Polynote: an IDE-inspired polyglot notebook

We are pleased to announce the open-source launch of Polynote: a new, polyglot notebook with first-class Scala support, Apache Spark integration, multi-language interoperability including Scala, Python, and SQL, as-you-type autocomplete, and more. Polynote provides data scientists and machine learning researchers with a notebook environment…

How Netflix microservices tackle dataset pub-sub

Introduction In a microservice architecture such as Netflix’s, propagating datasets from a single source to multiple downstream destinations can be challenging. These datasets can represent anything from service configuration to the results of a batch job, are often needed in-memory to optimize access and must be updated as…

Analytics at Netflix: Who we are and what we do

by Molly Jackman & Meghana Reddy Explained: 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 on what that term “data analytics” actually means — or what to call the people responsible for the work. Even within…

Performance comparison of video coding standards: an adaptive streaming perspective

“This is my advice to people: Learn how to cook, try new recipes, learn from your mistakes, be fearless, and above all have fun” — Julia Child (American chef, author, and television personality) At Netflix, we are continually refining the recipes we use to serve your favorite shows and movies…

Keeping Customers Streaming — The Centralized Site Reliability Practice at Netflix

We’re privileged to be in the business of bringing joy to our customers at Netflix. Whether it’s a compelling new series or an innovative product feature, we strive to provide a best-in-class service that people love and can enjoy anytime, anywhere…

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question — “Can I run a check myself to understand what data is behind…

Key Challenges with Quasi Experiments at Netflix

At 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 one methodology…

Growth Engineering at Netflix — Automated Imagery Generation

Background There’s a good chance you’ve probably visited the Netflix homepage. In the Growth Engineering team, we refer to this as the top of the signup funnel. For more background on the signup funnel and Growth Engineering’s role in the signup funnel, please read our initial post on…

Reimagining Experimentation Analysis at Netflix

Another day, another custom script to analyze an A/B test. Maybe you’ve done this before and have an old script lying around. If it’s new, it’s probably going to take some time to set up, right? Not at Netflix. ABlaze: The standard view…

Full Cycle Developers at Netflix

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…

Telltale: Netflix Application Monitoring Simplified

Our Telltale Vision An 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…

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…

A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix

Source credit: Netflix Within the rapid expansion of data-related roles in the last decade, the title Data Scientist has emerged as an umbrella term for myriad skills and areas of business focus. What does this title mean within a given company, or even within a given industry…

Our learnings from adopting GraphQL

by Artem Shtatnov and Ravi Srinivas Ranganathan In an earlier blog post, we provided a high-level overview of some of the applications in the Marketing Technology team that we build to enable scale and intelligence in driving our global advertising, which reaches users on sites like The New York Times…

Unbundling Data Science Workflows with Metaflow and AWS Step Functions

tl;dr Today, we are releasing a new job scheduler integration with AWS Step Functions. This integration allows the users of Metaflow to schedule their production workflows using a highly available, scalable, maintenance-free service, without…

Spinnaker Sets Sail to the Continuous Delivery Foundation

Since releasing Spinnaker to the open source community in 2015, the platform has flourished with the addition of new cloud providers, triggers, pipeline stages, and much more. Myriad new features, improvements, and innovations have been added by an ever growing, actively engaged community. Each new innovation has…