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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience. This approach has a handful of benefits.

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

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 2 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Picture yourself enthralled by the latest episode of your beloved Netflix series, delighting in an uninterrupted, high-definition streaming experience. This is where large-scale system migrations come into play.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

Part 3: System Strategies and Architecture By: VarunKhaitan With special thanks to my stunning colleagues: Mallika Rao , Esmir Mesic , HugoMarques This blog post is a continuation of Part 2 , where we cleared the ambiguity around title launch observability at Netflix. The response schema for the observability endpoint.

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RabbitMQ vs. Kafka: Key Differences

Scalegrid

This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. RabbitMQ follows a message broker model with advanced routing, while Kafkas event streaming architecture uses partitioned logs for distributed processing. What is RabbitMQ? What is Apache Kafka?

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Introducing Impressions at Netflix

The Netflix TechBlog

Architecture Overview The first pivotal step in managing impressions begins with the creation of a Source-of-Truth (SOT) dataset. The enriched data is seamlessly accessible for both real-time applications via Kafka and historical analysis through storage in an Apache Iceberg table.

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Best Practices for Scaling RabbitMQ

Scalegrid

Scaling RabbitMQ ensures your system can handle growing traffic and maintain high performance. Optimizing RabbitMQ performance through strategies such as keeping queues short, enabling lazy queues, and monitoring health checks is essential for maintaining system efficiency and effectively managing high traffic loads.

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Unlock end-to-end observability insights with Dynatrace PurePath 4 seamless integration of OpenTracing for Java

Dynatrace

Cloud-native technologies and microservice architectures have shifted technical complexity from the source code of services to the interconnections between services. Heterogeneous cloud-native microservice architectures can lead to visibility gaps in distributed traces. Dynatrace news.

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