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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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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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Practical API Design at Netflix, Part 1: Using Protobuf FieldMask

The Netflix TechBlog

When we process a request it is often beneficial to know which fields the caller is interested in and which ones they ignore. How can we achieve a similar functionality when designing our gRPC APIs? By default, gRPC uses protobuf as its IDL (interface definition language) and data serialization protocol.

Design 245
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Dynatrace observability is now available for Red Hat OpenShift on the IBM® Power® architecture

Dynatrace

We’re delighted to share that IBM and Dynatrace have joined forces to bring the Dynatrace Operator, along with the comprehensive capabilities of the Dynatrace platform, to Red Hat OpenShift on the IBM Power architecture (ppc64le). Dynatrace webhook server validates Dynakube definitions for correctness.

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Team Responsibility Ownership Patterns. Part 1: A Business Architecture Model

Strategic Tech

Team Topologies has significantly advanced the discussion on organisation design for technology companies. I’m not precious about any of the definition used in this article. My goal is to create common ground so that we can discuss the trade-offs of organisational design patterns. Processes are also a hierarchical concept.

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Microservices: A quick and simple definition

O'Reilly Software

Sam Newman provides a succinct definition of microservices in Building Microservices : “Microservices are small, autonomous services that work together.”. Built and released with automated processes. think in these terms: the smaller the service, the more you maximize the benefits and downsides of microservice architecture.

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The history of Grail: Why you need a data lakehouse

Dynatrace

Grail architectural basics. The aforementioned principles have, of course, a major impact on the overall architecture. A data lakehouse addresses these limitations and introduces an entirely new architectural design. Ingest and process with Grail. It’s based on cloud-native architecture and built for the cloud.