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Efficient Multimodal Data Processing: A Technical Deep Dive

DZone

In this article, I will walk through a comprehensive end-to-end architecture for efficient multimodal data processing while striking a balance in scalability, latency, and accuracy by leveraging GPU-accelerated pipelines, advanced neural networks , and hybrid storage platforms.

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

Dynatrace

This local SaaS presence minimizes latency and maximizes the speed and reliability of data access. As a SaaS vendor, Dynatrace carefully manages its deployments across different regions, assuring the efficient and optimal use of infrastructure to serve and support Dynatrace platform customers.

Azure 278
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Optimizing Database Performance in Middleware Applications

DZone

Efficient database operations in middleware can dramatically improve overall system performance, reduce latency, and enhance user experience. This is crucial because middleware often serves as the bridge between client applications and backend databases, handling a high volume of requests and data processing tasks.

Database 222
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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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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 247
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How to Scale Elasticsearch to Solve Your Scalability Issues

DZone

One such open-source, distributed search and analytics engine is Elasticsearch, which is very efficient at handling data in large sets and high-velocity queries. This extra network overhead will easily result in increased latency compared to a single-node architecture where data access is straightforward.

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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