扩展全球叙事：现代化 Netflix 的本地化分析

Each 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 presented at the Summit highlighting the breadth and impact of Analytics work across different areas of the business.

每年，我们将 Analytics Engineering 社区聚集在一起，举办 Analytics Summit — 一个多天的内部会议，分享 Netflix 各部门的 analytical deliverables，讨论 analytic practice，并建立社区内的关系。本文是 Summit 上呈现的几个主题之一，突显了 Analytics 工作在业务不同领域的广度和影响。

At Netflix, our goal is to entertain the world, which means we must speak the world’s languages. Given the company’s growth to serving 300 million+ members in more than 190+ countries and 50+ languages, the Localization team has had to scale rapidly in creating more dubs and subtitle assets than ever before. However, this growth created technical debt within our systems: a fragmented landscape of analytics workflows, duplicated pipelines, and siloed dashboards that we are now actively modernizing.

在 Netflix，我们的目标是娱乐世界，这意味着我们必须讲世界上所有的语言。鉴于公司发展到服务 300 million+ 会员，覆盖 190+ 个国家和 50+ 种语言，Localization 团队不得不快速扩展，创建比以往更多的 dubs 和 subtitle assets。然而，这种增长在我们的系统中造成了 technical debt：analytics workflows 的碎片化景观、重复的 pipelines，以及 siloed dashboards，我们现在正在积极对其进行现代化。

The Challenge: “Who Made This Dub?”

挑战：“Who Made This Dub?”

Historically, business logic for localization metrics was replicated across isolated domains. A question as simple as “Who made this dub/subtitle?” is actually complex — it requires mapping multiple data sources through intricate and constantly changing logic, which varies depending on the specific language asset type and creation workflow.

历史上，localization metrics 的 business logic 在孤立的 domains 中被复制。一个像 “Who made this dub/subtitle?” 这样简单的问题实际上很复杂 — 它需要通过复杂且不断变化的 logic 映射多个 data sources，这种 logic 取决于特定的 language asset type 和 creation workflow。

When this logic is copied i...