How Our Paths Brought Us to Data and Netflix

by Julie Beckley & Chris Pham This 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 us to the right! [Chris] Julie and I joined the Streaming DSE team at Netflix…

Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

tl;dr Metaflow is now open-source! Get started at metaflow.org. Netflix applies data science to hundreds of use cases across the company, including optimizing content delivery and video encoding. Data scientists at Netflix relish our culture that…

Lerner — using RL agents for test case scheduling

Introduction Netflix brings delightful customer experiences to homes on a variety of devices that continues to grow each day. Partners across the globe leverage Netflix device certification process on a continual basis to ensure that quality products and experiences are…

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…

Full Cycle Developers at Netflix

Netflix Hack Day — November 2019

Hack Day at Netflix is an opportunity to build and show off a feature, tool, or quirky app. The goal is simple: experiment with new ideas/technologies, engage with colleagues across different disciplines, and have fun! We know even the silliest idea…

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 …

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…

Netflix at MIT CODE 2020

In November, Netflix was a proud sponsor of the 2020 Conference on Digital Experimentation (CODE), hosted by the MIT Initiative on the Digital Economy. As well as providing sponsorship, Netflix data scientists were active participants, with three contributions. Eskil Forsell and colleagues presented a poster describing Success stories…

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…

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…

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…

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…

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…

Evolving Container Security With Linux User Namespaces

As previously discussed on the Netflix Tech Blog, Titus is the Netflix container orchestration system. It runs a wide variety of workloads from various parts of the company — everything…

Evolution of Application Data Caching : From RAM to SSD

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…

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…

Applying Netflix DevOps Patterns to Windows

By Justin Phelps and Manuel Correa Customizing Windows images at Netflix was a manual, error-prone, and time consuming process. In this blog post, we describe how we improved the methodology, which technologies we leveraged, and how this has improved service deployment and consistency. Artisan Crafted Images In the Netflix…

Beyond REST

by Dane Avilla The entertainment industry has struggled with COVID-19 restrictions impacting productions around the globe. Since early 2020, Netflix has been iteratively developing systems to provide internal stakeholders and business leaders with up-to-date tools and dashboards with the latest information on the pandemic. These software solutions allow executive leadership…

How Netflix uses eBPF flow logs at scale for network insight

Netflix has developed a network observability sidecar called Flow Exporter that uses eBPF tracepoints to capture TCP flows at near real time. At much less than 1% of CPU and memory on the instance, this highly performant sidecar provides…

Data Engineers of Netflix — Interview with Samuel Setegne

This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Samuel Setegne is a Senior Software Engineer on the Core Data Science and Engineering team. Samuel and his team build tools and frameworks…

Optimizing data warehouse storage

Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3, and each day we ingest and create additional Petabytes. At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions)…

The Netflix Cosmos Platform

by Frank San Miguel on behalf of the Cosmos team Introduction Cosmos is a computing platform that combines the best aspects of microservices with asynchronous workflows and serverless functions. Its sweet spot is applications that involve resource-intensive algorithms coordinated via complex, hierarchical workflows that last anywhere from minutes to years. It…

Evolving Regional Evacuation

At Netflix we prioritize innovation and velocity in pursuit of the best experience for our 150+ million global customers. This means that our microservices constantly evolve and change, but what doesn’t change is our responsibility to provide a highly available service that delivers 100+ million hours of daily streaming to…