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Data Mesh?—?A Data Movement and Processing Platform @ Netflix

The Netflix TechBlog

A Data Movement and Processing Platform @ Netflix By Bo Lei , Guilherme Pires , James Shao , Kasturi Chatterjee , Sujay Jain , Vlad Sydorenko Background Realtime processing technologies (A.K.A stream processing) is one of the key factors that enable Netflix to maintain its leading position in the competition of entertaining our users.

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Part 2: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

Content CashModeling Alex Diamond At Netflix we produce a variety of entertainment: movies, series, documentaries, stand-up specials, and more. Each format has a different production process and different patterns of cash spend, called our Content Forecast. A sizable portion of our Content Forecast is represented by TBDSlots.

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Helping VFX studios pave a path to the cloud

The Netflix TechBlog

Rachel Kelley (AWS), Ranjit Raju (AWS) Rendering is core to the the VFX process VFX studios around the world create amazing imagery for Netflix productions. This ultimately results in more compelling entertainment for Netflix members. This program is just one example of the many ways Netflix strives to entertain the world.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

As a result, requests are uniformly handled, and responses are processed cohesively. This data is processed from a real-time impressions stream into a Kafka queue, which our title health system regularly polls. This centralized format, defined and maintained by our team, ensures all endpoints adhere to a consistent protocol.

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Netflix Studio Engineering Overview

The Netflix TechBlog

In an effort to effectively and efficiently produce this content we are looking to improve and automate many areas of the production process. We combine our entertainment knowledge and our technical expertise to provide innovative technical solutions from the initial pitch of an idea to the moment our members hit play.

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Foundation Model for Personalized Recommendation

The Netflix TechBlog

The impetus for constructing a foundational recommendation model is based on the paradigm shift in natural language processing (NLP) to large language models (LLMs). To harness this data effectively, we employ a process of interaction tokenization, ensuring meaningful events are identified and redundancies are minimized.

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My (Seemingly) Random Walk to Netflix

The Netflix TechBlog

That person grew up dreaming of working in the entertainment industry. 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?!