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Dynatrace on Microsoft Azure in Australia enables regional customers to leverage AI-powered observability

Dynatrace

As of October 2024, Dynatrace is available on Microsoft Azure Australia East region, enabling joint customers to maintain a local SaaS presence. This local SaaS presence minimizes latency and maximizes the speed and reliability of data access. The result? Optimized performance and enhanced customer experiences.

Azure 278
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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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Understanding and Managing Latency in APISIX: A Comprehensive Technical Guide

DZone

A common query from users revolves around the precise measurement of latency in APISIX. When utilizing APISIX, how should one address unusually high latency? In reality, discussions on latency measurement are centered around the performance and response time of API requests.

Latency 261
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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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Introducing Impressions at Netflix

The Netflix TechBlog

This dual-path approach leverages Kafkas capability for low-latency streaming and Icebergs efficient management of large-scale, immutable datasets, ensuring both real-time responsiveness and comprehensive historical data availability. million impression events globally every second, with each event approximately 1.2KB in size.

Tuning 166
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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently. In a serverless architecture, applications are distributed to meet demand and scale requirements efficiently.

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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. These insights have shaped the design of our foundation model, enabling a transition from maintaining numerous small, specialized models to building a scalable, efficient system.

Tuning 165