Remove Efficiency Remove Infrastructure Remove Tuning
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New integrations announced at AWS re:Invent enhance cloud performance, security, and automation

Dynatrace

These innovations promise to streamline operations, boost efficiency, and offer deeper insights for enterprises using AWS services. This seamless integration accelerates cloud adoption, allowing enterprises to maximize the value of their AWS infrastructure and focus on innovation rather than managing observability configurations.

AWS 298
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Foundation Model for Personalized Recommendation

The Netflix TechBlog

It facilitates the distribution of these learnings to other models, either through shared model weights for fine tuning or directly through embeddings. In NLP, the trend is moving away from numerous small, specialized models towards a single, large language model that can perform a variety of tasks either directly or with minimal fine-tuning.

Tuning 179
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Power Dashboarding, Part I: Start your exploration journey with Dashboards

Dynatrace

With Dashboards , you can monitor business performance, user interactions, security vulnerabilities, IT infrastructure health, and so much more, all in real time. Even if infrastructure metrics aren’t your thing, you’re welcome to join us on this creative journey simply swap out the suggested metrics for ones that interest you.

Metrics 243
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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

However, this category requires near-immediate access to the current count at low latencies, all while keeping infrastructure costs to a minimum. Eventually Consistent : This category needs accurate and durable counts, and is willing to tolerate a slight delay in accuracy and a slightly higher infrastructure cost as a trade-off.

Latency 251
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Kafka scales efficiently for large data workloads, while RabbitMQ provides strong message durability and precise control over message delivery. Message brokers handle validation, routing, storage, and delivery, ensuring efficient and reliable communication. This allows Kafka clusters to handle high-throughput workloads efficiently.

Latency 147
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Part 1: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

We kick off with a few topics focused on how were empowering Netflix to efficiently produce and effectively deliver high quality, actionable analytic insights across the company. Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft.

Analytics 212
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Now let’s look at how we designed the tracing infrastructure that powers Edgar. This insight led us to build Edgar: a distributed tracing infrastructure and user experience. Our distributed tracing infrastructure is grouped into three sections: tracer library instrumentation, stream processing, and storage.