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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

Our previous blog post presented replay traffic testing — a crucial instrument in our toolkit that allows us to implement these transformations with precision and reliability. Behind these perfect moments of entertainment is a complex mechanism, with numerous gears and cogs working in harmony.

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Now let’s look at how we designed the tracing infrastructure that powers Edgar. Now let’s look at how we designed the tracing infrastructure that powers Edgar. a Netflix member via Twitter This is an example of a question our on-call engineers need to answer to help resolve a member issue?—?which

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. These internal libraries process data by capturing the changed partitions, which works only on specific use cases. Introduction Netflix relies on data to power its business in all phases.

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Feature flags done right with the OpenFeature initiative and Dynatrace

Dynatrace

In software development, feature flags are an established path to rapid value, continuous progressive delivery, and safe deployments. The ability to isolate certain software capabilities makes it easier to test, preview, release, and roll back small functional increments. But feature flagging can also introduce some issues.

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Evolution of ML Fact Store

The Netflix TechBlog

We will share how its design has evolved over the years and the lessons learned while building it. Figure 1 below shows how Axion interacts with Netflix’s ML platform. Figure 1 below shows how Axion interacts with Netflix’s ML platform. To achieve this, we rely on Machine Learning (ML) algorithms.

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Building a Media Understanding Platform for ML Innovations

The Netflix TechBlog

Earlier we shared the details of one of these algorithms , introduced how our platform team is evolving the media-specific machine learning ecosystem , and discussed how data from these algorithms gets stored in our annotation service. Use cases Use case #1: Dialogue search Dialogue is a central aspect of storytelling.

Media 291
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Mastering MongoDB® Timeout Settings

Scalegrid

How the MongoDB timeout is set up can significantly affect your application’s performance, no matter if you are an experienced MongoDB user or just starting with NoSQL databases. Timeout errors can occur due to different factors like network problems, server or client setting misconfiguration, and write operation delays.

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