Part 3: System Strategies and Architecture By: Varun Khaitan With special thanks to my stunning colleagues: Mallika Rao, Esmir Mesic, Hugo Marques This blog post is a continuation of Part 2, where we cleared the ambiguity around title launch observability at Netflix. In this installment, we will explore the strategies, tools, and methodologies that were employed to achieve comprehensive title observability at scale. Defining the observability endpoint To create a comprehensive solution, we decided to introduce observability endpoints first. Each microservice involved in our Pers ..read more

Part 1: Creating the Source of Truth for Impressions By: Tulika Bhatt Imagine scrolling through Netflix, where each movie poster or promotional banner competes for your attention. Every image you hover over isn’t just a visual placeholder; it’s a critical data point that fuels our sophisticated personalization engine. At Netflix, we call these images ‘impressions,’ and they play a pivotal role in transforming your interaction from simple browsing into an immersive binge-watching experience, all tailored to your unique tastes. Capturing these moments and turning them into a personaliz ..read more

Part 2: Navigating Ambiguity By: Varun Khaitan With special thanks to my stunning colleagues: Mallika Rao, Esmir Mesic, Hugo Marques Building on the foundation laid in Part 1, where we explored the “what” behind the challenges of title launch observability at Netflix, this post shifts focus to the “how.” How do we ensure every title launches seamlessly and remains discoverable by the right audience? In the dynamic world of technology, it’s tempting to leap into problem-solving mode. But the key to lasting success lies in taking a step back — understanding the broader context before d ..read more

This article is the last in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Need to catch up? Check out Part 1, which detailed how we’re empowering Netflix to efficiently produce and effectively deliver high quality, actionable analytic insights across the company and Part 2, which stepped through a few exciting business applications for Analytics Engineering. This post will go into aspects of technical craft. Dashboard Design Tips Rina Chang, Susie Lu What is design, a ..read more

This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Need to catch up? Check out Part 1. In this article, we highlight a few exciting analytic business applications, and in our final article we’ll go into aspects of the technical craft. Game Analytics Yimeng Tang, Claire Willeck, Sagar Palao User Acquisition Incrementality for Netflix Games Netflix has been launching games for the past three years, during which it has initiated various marketing ..read more

David J. Berg*, David Casler^, Romain Cledat*, Qian Huang*, Rui Lin*, Nissan Pow*, Nurcan Sonmez*, Shashank Srikanth*, Chaoying Wang*, Regina Wang*, Darin Yu* *: Model Development Team, Machine Learning Platform ^: Content Demand Modeling Team A month ago at QConSF, we showcased how Netflix utilizes Metaflow to power a diverse set of ML and AI use cases, managing thousands of unique Metaflow flows. This followed a previous blog on the same topic. Many of these projects are under constant development by dedicated teams with their own business goals and development best practices, such as t ..read more

This article is the first in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. We kick off with a few topics focused on how we’re empowering Netflix to efficiently produce and effectively deliver high quality, actionable analytic insights across the company. Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft. At Netflix, we seek to entertain the world by ensuring our members find the shows and movies that wi ..read more

By J Han, Pallavi Phadnis Context At Netflix, we use Amazon Web Services (AWS) for our cloud infrastructure needs, such as compute, storage, and networking to build and run the streaming platform that we love. Our ecosystem enables engineering teams to run applications and services at scale, utilizing a mix of open-source and proprietary solutions. In turn, our self-serve platforms allow teams to create and deploy, sometimes custom, workloads more efficiently. This diverse technological landscape generates extensive and rich data from various infrastructure entities, from which, data engi ..read more

Part 1: Understanding The Challenges By: Varun Khaitan With special thanks to my stunning colleagues: Mallika Rao, Esmir Mesic, Hugo Marques Introduction At Netflix, we manage over a thousand global content launches each month, backed by billions of dollars in annual investment. Ensuring the success and discoverability of each title across our platform is a top priority, as we aim to connect every story with the right audience to delight our members. To achieve this, we are committed to building robust systems that deliver comprehensive observability, enabling us to take full account ..read more

By: Rajiv Shringi, Oleksii Tkachuk, Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction, a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction. This counting service, built on top of the TimeSeries Abstraction, enables distributed counting at scale while maintaining similar low latency performance. As with all our abstractions, we use our Data Gateway Control Plane to shard, configure, and deploy this serv ..read more