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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. It facilitates the distribution of these learnings to other models, either through shared model weights for fine tuning or directly through embeddings.

Tuning 165
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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. Apache Kafka uses a custom TCP/IP protocol for high throughput and low latency.

Latency 147
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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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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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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Such frameworks support software engineers in building highly scalable and efficient applications that process continuous data streams of massive volume. Stream processing systems, designed for continuous, low-latency processing, demand swift recovery mechanisms to tolerate and mitigate failures effectively.

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Best Practices for Scaling RabbitMQ

Scalegrid

Key Takeaways RabbitMQ improves scalability and fault tolerance in distributed systems by decoupling applications, enabling reliable message exchanges. This decoupling is crucial in modern architectures where scalability and fault tolerance are paramount. Keeping queues short maintains a responsive and efficient RabbitMQ setup.

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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. Logging is selective to cases where the old and new responses do not match.

Traffic 347