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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. The Netflix video processing pipeline went live with the launch of our streaming service in 2007. The Netflix video processing pipeline went live with the launch of our streaming service in 2007.

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What I learned at GlueCon 2023?—?Tipping Points and Generative AI

Adrian Cockcroft

One example is that Netflix launched it’s streaming service in 2007, just at the point when the cost of streaming a movie over the network (which was dropping fast) became less than the cost of shipping a DVD. Rob Hirschfeld of RackN had this perspective on the impact of AI on his domain of infrastructure automation.

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MSMQ is dead

Particular Software

So as a company dedicated to making our customers better at building, maintaining, and running complex software systems, it's basically impossible for us to recommend building a new system using MSMQ. What if you have a current system already running on MSMQ? So what do I need to do? What do you do then? for more details.

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Is MongoDB Open Source? Is Planet Earth Flat?

Percona

MongoDB started out in 2007 as 10gen, a New York-based company looking to create a Platform as a Service (PaaS) solution. Instead of relational (SQL) databases defined primarily through a hierarchy of related sets via tables and columns, their non-relational structure used a system of collections and documents.

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DevOps automation: From event-driven automation to answer-driven automation [with causal AI]

Dynatrace

We will also explore the evolution of DevOps automation and the significance of data-driven answers in unlocking streamlined, automated DevOps and SRE processes. Business process automation Business process automation is the foundation for improving operational efficiency. But it doesn’t stop there.

DevOps 219
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Scaling Media Machine Learning at Netflix

The Netflix TechBlog

Our goal in building a media-focused ML infrastructure is to reduce the time from ideation to productization for our media ML practitioners. We accomplish this by paving the path to: Accessing and processing media data (e.g. We accomplish this by paving the path to: Accessing and processing media data (e.g.

Media 290
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The Netflix Cosmos Platform

The Netflix TechBlog

Background The Media Cloud Engineering and Encoding Technologies teams at Netflix jointly operate a system to process incoming media files from our partners and studios to make them playable on all devices. The first generation of this system went live with the streaming launch in 2007. Delivery?—?A