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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 202
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Data Movement in Netflix Studio via Data Mesh

The Netflix TechBlog

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. As of now, CDC sources have been implemented for data stores at Netflix (MySQL, Postgres).

Big Data 253
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Optimizing data warehouse storage

The Netflix TechBlog

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture.

Storage 203
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Web Performance Bookshelf

Rigor

Take, for example, The Web Almanac , the golden collection of Big Data combined with the collective intelligence from most of the authors listed below, brilliantly spearheaded by Google’s @rick_viscomi. Information Architecture. Web Performance Tuning. Web Performance Daybook-Volume-2. Progressive Web Apps.