• Netflix Introduces New Specialization and Media Data Lake: Netflix has launched a new engineering specialization - Media ML Data Engineering, along with a Media Data Lake. This is designed to handle large-scale video, audio, text, and image assets. Early results show richer ML models, faster evaluation cycles, and deeper insights into creative workflows.
• Evolution of Data Engineering Function: In a recent blog post, the company described how this evolution moves its data engineering function from "facts and metrics" tables to directly supporting machine learning on media content.
• Purpose and Goals: By formalizing the role and platform, Netflix aims to provide standardized, ML-ready datasets and enable faster experimentation in areas like localization, media restoration, ratings, and multimodal search.
• Previous Focus and Current Challenge: The data engineering team used to focus on structured tables for metrics, dashboards, and models. But as studio operations expanded, they faced a flood of multi-modal, unstructured media, which traditional pipelines couldn't manage.
• Creation of Media ML Data Engineering: To meet this challenge, Netflix created this specialization at the intersection of data engineering, ML infrastructure, and media production. These engineers build and maintain pipelines for the Media Data Lake, standardize assets, enrich metadata, and expose ML-ready corpora.
• Media Data Lake and Its Components: The Media Data Lake is designed for storing and serving media assets and metadata, powered by LanceDB and integrated into Netflix's big data ecosystem. It has a Media Table that captures metadata and references to assets and can store ML outputs. Supporting components include a standardized data model, a pythonic Data API, UI tools, and systems for real-time and batch processing.
• Powered Applications: These tables already power several applications like translation, audio quality metrics, HDR video restoration, compliance checks, and multimodal search.
• Initial Rollout and Future Plans: Netflix started with a scoped "data pond" and then expanded. They plan to further expand the Media Data Lake and share future learnings with the data engineering community. They also highlight the benefits already emerging such as richer ML models, faster evaluation cycles, and deeper insights.