Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix
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 stories they love.To connect stories with diverse audiences around the world, we produce promotiona...
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 incorporating components from the Kubernetes ecosystem into our container platform Titus. One exam...
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 built an automated data canary system that validates data transformations using production traffic....
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 tens of thousands of scheduled workloads running across our orchestration systems. Behind each of thes...
Predicting Risk in Content Launches: How Data-Driven Insights can Transform Launch Planning
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 practice, and build relationships within the community. This post is one of several topics present...
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. Historically, several bespoke Data Movement connectors were developed across different engineering org...
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, which operates automatically and quickly with little effort, and System 2, which allocates attention...