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

The Netflix TechBlog

Behind these perfect moments of entertainment is a complex mechanism, with numerous gears and cogs working in harmony. Replay traffic testing gives us the initial foundation of validation, but as our migration process unfolds, we are met with the need for a carefully controlled migration process.

Traffic 279
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Business observability and the travel and hospitality industry: a key to successful recovery

Dynatrace

The importance of factors such as price, safety, convenience, change fees, loyalty points, entertainment – the list can be very long – varies from one customer to the next, and may even change from visit to visit. During the booking process, I attempted to use some of my travel vouchers – but the button to apply these credits didn’t work.

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Business observability and the travel and hospitality industry: a key to successful recovery

Dynatrace

The importance of factors such as price, safety, convenience, change fees, loyalty points, entertainment – the list can be very long – varies from one customer to the next, and may even change from visit to visit. During the booking process, I attempted to use some of my travel vouchers – but the button to apply these credits didn’t work.

Airlines 197
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training. We use metaflow.Table to resolve all input shards which are distributed to Metaflow tasks which are responsible for processing terabytes of data collectively.

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Supporting content decision makers with machine learning

The Netflix TechBlog

To mitigate this uncertainty, executives throughout the entertainment industry have always consulted historical data to help characterize the potential audience of a title using comparable titles, if they exist. Similar titles In entertainment, it is common to contextualize a new project in terms of existing titles.

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Telltale: Netflix Application Monitoring Simplified

The Netflix TechBlog

A metric crossed a threshold. Metrics are a key part of understanding application health. But sometimes you can have too many metrics, too many graphs, and too many dashboards. Telltale uses a variety of signals from multiple sources to assemble a constantly evolving model of the application’s health: Atlas time series metrics.