As we continue to grow here at Netflix, the needs of Revenue and Growth Engineering are rapidly evolving; and our tools must also evolve just as rapidly. The Revenue and Growth Tools (RGT) team decided to set off on a journey to build tools in an abstract manner to have solutions readily available within our organization. We identified common design patterns and architectures scattered across various tools which were all duplicating efforts in some way or another.

We needed to consolidate these tools in a way that scaled with the teams we served. It needed to have the agility of a micro frontend and the extensibility of a framework to empower our stakeholders to extend our tools. We would abstract parts of which anyone can then customize, or extend, to meet their specific business or technical requirements. The end result is Lattice: RGT’s pluggable framework for micro frontends.

Behind the scenes of the beloved Netflix streaming service and content, there are many technology innovations in media processing. Packaging has always been an important step in media processing. After content ingestion, inspection and encoding, the packaging step encapsulates encoded video and audio in codec agnostic container formats and provides features such as audio video synchronization, random access and DRM protection. Our previous tech blog Packaging award-winning shows with award-winning technology detailed our packaging technology deployed on the streaming side.

This introduction is the first in a multi-part series on how Netflix uses A/B tests to make decisions that continuously improve our products, so we can deliver more joy and satisfaction to our members. Subsequent posts will cover the basic statistical concepts underpinning A/B tests, the role of experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix.

This is the second post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. See here for Part 1: Decision Making at Netflix. Subsequent posts will go into more details on the statistics of A/B tests, experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix.

In our previous post, we discussed how we utilize FieldMask as a solution when designing our APIs so that consumers can request the data they need when fetched via gRPC. In this blog post we will continue to cover how Netflix Studio Engineering uses FieldMask for mutation operations such as update and remove.

A Journey About Productizing Security.

At Netflix, we heavily use gRPC for the purpose of backend to backend communication. When we process a request it is often beneficial to know which fields the caller is interested in and which ones they ignore. Some response fields can be expensive to compute, some fields can require remote calls to other services. Remote calls are never free; they impose extra latency, increase probability of an error, and consume network bandwidth. How can we understand which fields the caller doesn’t need to be supplied in the response, so we can avoid making unnecessary computations and remove calls? With GraphQL this comes out of the box through the use of field selectors. In the JSON:API standard a similar technique is known as Sparse Fieldsets. How can we achieve a similar functionality when designing our gRPC APIs? The solution we use within the Netflix Studio Engineering is protobuf FieldMask.

At Netflix, hundreds of different device types, from streaming sticks to smart TVs, are tested every day through automation to ensure that new software releases continue to deliver the quality of the Netflix experience that our customers enjoy. In addition, Netflix continuously works with its partners (such as Roku, Samsung, LG, Amazon) to port the Netflix SDK to their new and upcoming devices (TVs, smart boxes, etc), to ensure the quality bar is reached before allowing the Netflix application on the device to go out into the world. The Partner Infrastructure team at Netflix provides solutions to support these two significant efforts by enabling device management at scale.

复盘Netflix发展历程、产品策略和财务表现对于分析中国国内长视频行业具有一定借鉴意义。

只有当我们用正确的流失数据才有可能通过分析来正确的指导决策。本文介绍的错误都是很常见的哦！​

Data Mesh is a fully managed, streaming data pipeline product used for enabling Change Data Capture (CDC) use cases. In Data Mesh, users create sources and construct pipelines. Sources mimic the state of an externally managed source — as changes occur in the external source, corresponding CDC messages are produced to the Data Mesh source. Pipelines can be configured to transform and store data to externally managed sinks.

Data Mesh provides a drag-and-drop, self-service user interface for exploring sources and creating pipelines so that users can focus on delivering business value without having to worry about managing and scaling complex data streaming infrastructure.

到今天，被验证的互联网商业模式只有三种：

（1）直接销售模式（Direct Sales）

（2）广告支持模式（Advertising Supported）

（3）价值分享模式（Shared Value）

在异步工作流中实现可观察性。

在这篇文章中，我们将介绍Netflix Drive，一个用于媒体资产的云驱动器，并对它的一些功能和界面做一个高度概括。我们打算把这篇文章作为介绍Netflix Drive的系列文章中的第一篇文章。在未来的文章中，我们将对Netflix Drive的几个组件进行架构上的深入研究。

在这篇文章中，我们将分享Netflix如何提供技术和基础设施，帮助制作人员在制作和后期制作阶段创建和交换媒体的幕后故事。我们还将介绍我们的 Studio Engineering 工作如何通过利用我们基于云的服务，帮助 Netflix 制作人员在媒体物流上花费更少的时间。

打造“自由与责任”企业文化的3个步骤。