By Andrew Nguonly, Armando Magalhães, Obi-Ike Nwoke, Shervin Afshar, Sreyashi Das, Tongliang Liu, Wei Liu, Yucheng Zeng

Background

Over the next few years, most content on Netflix will come from Netflix’s own Studio. From the moment a Netflix film or series is pitched and long before it becomes available on Netflix, it goes through many phases. This happens at an unprecedented scale and introduces many interesting challenges; one of the challenges is how to provide visibility of Studio data across multiple phases and systems to facilitate operational excellence and empower decision making. …


This post is part of our “Data Engineers of Netflix” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix.

Kevin Wylie is a Data Engineer on the Content Data Science and Engineering team. In this post, Kevin talks about his extensive experience in content analytics at Netflix since joining more than 10 years ago.

Kevin grew up in the Washington, DC area, and received his undergraduate degree in Mathematics from Virginia Tech. Before joining Netflix, he worked at MySpace, helping implement page categorization, pathing analysis, sessionization, and more. …


By Burak Bacioglu, Meenakshi Jindal

Asset Management at Netflix

At Netflix, all of our digital media assets (images, videos, text, etc.) are stored in secure storage layers. We built an asset management platform (AMP), codenamed Amsterdam, in order to easily organize and manage the metadata, schema, relations and permissions of these assets. It is also responsible for asset discovery, validation, sharing, and for triggering workflows.

Amsterdam service utilizes various solutions such as Cassandra, Kafka, Zookeeper, EvCache etc. In this blog, we will be focusing on how we utilize Elasticsearch for indexing and search the assets.

Amsterdam is built on top of three storage layers.


By Gim Mahasintunan on behalf of Data Platform Engineering.

Supporting a rapidly growing base of engineers of varied backgrounds using different data stores can be challenging in any organization. Netflix’s internal teams strive to provide leverage by investing in easy-to-use tooling that streamlines the user experience and incorporates best practices.

In this blog post, we are thrilled to share that we are open-sourcing one such tool: the Netflix Data Explorer. The Data Explorer gives our engineers fast, safe access to their data stored in Cassandra and Dynomite/Redis data stores.

Netflix Data Explorer on GitHub

History

We began this project several years…


A Script Authoring Specification

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, forced narratives, subtitles or any combination they prefer. Capturing creative vision and nuances in translation is critical to achieving this goal. Creating a dub or a subtitle is a complex, multi-step process that involves:

  • Transcribing and timing the dialogue in the original language from a completed show to create a…

By Alok Tiagi, Hariharan Ananthakrishnan, Ivan Porto Carrero and Keerti Lakshminarayan

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 flow data at scale for network insight.

Challenges

The cloud network infrastructure that Netflix utilizes today consists of AWS services such as VPC, DirectConnect, VPC Peering, Transit Gateways, NAT Gateways, etc and Netflix owned devices. Netflix software infrastructure is a large distributed ecosystem that consists of specialized functional tiers that are…


Part of our series on who works in Analytics at Netflix — and what the role entails

By Sean Barnes, Studio Production Data Science & Engineering

I am going to tell you a story about a person that works for Netflix. That person grew up dreaming of working in the entertainment industry. They attended the University of Southern California, double majored in data science and television & film production, and graduated summa cum laude. Upon graduation, they received an offer from Netflix to become an analytics engineer, and pursue their lifelong dream of orchestrating the beautiful synergy of analytics and entertainment. Pretty straightforward, right?!

Such a linear trajectory would make for a compelling candidate, but in reality…


Written by Colby Callahan, Megha Manohara, and Mike Azar.

Managing and operating asynchronous workflows can be difficult without the proper tools and architecture that puts observability, debugging, and tracing at the forefront.

Imagine getting paged outside normal work hours — users are having trouble with the application you’re responsible for, and you start diving into logs. However, they are scattered across multiple systems, and there isn’t an easy way to tie related messages together. Once you finally find useful identifiers, you may begin writing SQL queries against your production database to find out what went wrong. …


A file and folder interface for Netflix Cloud Services

Written by Vikram Krishnamurthy, Kishore Kasi, Abhishek Kapatkar, Tejas Chopra, Prudhviraj Karumanchi, Kelsey Francis, Shailesh Birari

In this post, we are introducing Netflix Drive, a Cloud drive for media assets and providing a high level overview of some of its features and interfaces. We intend this to be a first post in a series of posts covering Netflix Drive. In the future posts, we will do an architectural deep dive into the several components of Netflix Drive.

Netflix, and particularly Studio applications (and Studio in the Cloud) produce petabytes of data backed by billions of media assets. Several artists and…


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 that support data engineering teams across Netflix. In this post, Samuel talks about his journey from being a clinical researcher to supporting data engineering teams.

Samuel comes from West Philadelphia, and he received his Master’s in Biotechnology from Temple University. Before Netflix, Samuel worked at Travelers Insurance in the Data…

Netflix Technology Blog

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