I spend most of my professional time designing and building data products — analytical applications like data dashboards or data visualization prototypes for understanding algorithms or datasets. A significant but still much smaller amount of my time has been spent developing a charting framework known as Semiotic that is used for the charts in many of these data products. If you’re just intereste
By Michelle Ufford, M Pacer, Matthew Seal, and Kyle Kelley Notebooks have rapidly grown in popularity among data scientists to become the de facto standard for quick prototyping and exploratory analysis. At Netflix, we’re pushing the boundaries even further, reimagining what a notebook can be, who can use it, and what they can do with it. And we’re making big investments to help make this vision a
We want to provide an amazing experience to each member, winning the “moments of truth” where they decide what entertainment to enjoy. To do that, we need to understand the health of our system. To quickly and easily understand the health of the system, we need a simple metric that a diverse set of people can comprehend. In this post we will discuss how we discovered and aligned everyone around on
Colin McFarland, Michael Pow, Julia Glick Experimentation informs much of our decision making at Netflix. We design, analyze, and execute experiments with rigor so that we have confidence that the changes we’re making are the right ones for our members and our business. We have many years of experience running experiments in all aspects of the Netflix product, continually improving our UI, search,
by Eric Eiswerth Millions of people visit Netflix every day. Many of them are already Netflix members, looking to enjoy their favorite movies and TV shows, and we work hard to ensure they have a great experience. Others are not yet members, and are looking to better understand our service before signing up. These prospective members arrive from over 190 countries around the world, and each person
by Ajoy Majumdar, Zhen Li Most large companies have numerous data sources with different data formats and large data volumes. These data stores are accessed and analyzed by many people throughout the enterprise. At Netflix, our data warehouse consists of a large number of data sets stored in Amazon S3 (via Hive), Druid, Elasticsearch, Redshift, Snowflake and MySql. Our platform supports Spark, Pre
Our mission at Netflix is to deliver joy to our members by providing high-quality content, presented with a delightful experience. We are constantly innovating on our product at a rapid pace in pursuit of this mission. Our innovations span personalized title recommendations, infrastructure, and application features like downloading and customer profiles. Our growing global member base of 125 milli
by Chaitanya Ekanadham One of the common questions we get asked is: “Why do we need machine learning to improve streaming quality?” This is a really important question, especially given the recent hype around machine learning and AI which can lead to instances where we have a “solution in search of a problem.” In this blog post, we describe some of the technical challenges we face for video stream
In part 1 of this series, we introduced Scryer, Netflix’s predictive autoscaling engine, and discussed its use cases and how it runs in Netflix. In this second installment, we will discuss the design of Scryer ranging from the technical implementation to the algorithms that drive its predictions. Design of ScryerScryer has a simple data flow architecture. On a very high level, historical data flow
By Ashok Chandrashekar, Fernando Amat, Justin Basilico and Tony Jebara For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each mem
by Jason Chan Netflix is excited to be heading back to Las Vegas for AWS re:Invent at the end of the month! Many Netflix engineers and recruiters will be in attendance, and we’re looking forward to meeting and reconnecting with cloud enthusiasts and Netflix OSS users. We’re posting the schedule of Netflix talks here to make it a bit easier to find our speakers at re:Invent. We’ll also have a booth
Distributed systems create threats to resilience that are not addressed by classical approaches to development and testing. We’ve passed the point where individual humans can reasonably navigate these systems at scale. As we embrace a world that emphasizes automation and engineering over architecting, we left gaps open in our understanding of complex systems. Chaos Engineering is a new discipline
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