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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 251
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Scalable Annotation Service?—?Marken

The Netflix TechBlog

Scalable Annotation Service — Marken by Varun Sekhri , Meenakshi Jindal Introduction At Netflix, we have hundreds of micro services each with its own data models or entities. The service should be able to serve real-time, aka UI, applications so CRUD and search operations should be achieved with low latency.

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Why growing AI adoption requires an AI observability strategy

Dynatrace

An AI observability strategy—which monitors IT system performance and costs—may help organizations achieve that balance. They can do so by establishing a solid FinOps strategy. The post Why growing AI adoption requires an AI observability strategy appeared first on Dynatrace news. What is AI observability? Use containerization.

Strategy 288
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Title Launch Observability at Netflix Scale

The Netflix TechBlog

The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? The complexity of these operational demands underscored the urgent need for a scalable solution.

Traffic 172
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

This decoupling simplifies system architecture and supports scalability in distributed environments. Kafka stores and distributes data through a partitioned log system, which spans multiple brokers to provide fault tolerance and scalability. What is RabbitMQ? This allows Kafka clusters to handle high-throughput workloads efficiently.

Latency 147
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Best Practices for Scaling RabbitMQ

Scalegrid

Youll also learn strategies for maintaining data safety and managing node failures so your RabbitMQ setup is always up to the task. Key Takeaways RabbitMQ improves scalability and fault tolerance in distributed systems by decoupling applications, enabling reliable message exchanges.

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Foundation Model for Personalized Recommendation

The Netflix TechBlog

Yet, many are confined to a brief temporal window due to constraints in serving latency or training costs. Key insights from this shiftinclude: A Data-Centric Approach : Shifting focus from model-centric strategies, which heavily rely on feature engineering, to a data-centric one.

Tuning 165