Remove Data Remove Latency Remove Processing
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Efficient Multimodal Data Processing: A Technical Deep Dive

DZone

Multimodal data processing is the evolving need of the latest data platforms powering applications like recommendation systems, autonomous vehicles, and medical diagnostics. Handling multimodal data spanning text, images, videos, and sensor inputs requires resilient architecture to manage the diversity of formats and scale.

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

Dynatrace

As modern multicloud environments become more distributed and complex, having real-time insights into applications and infrastructure while keeping data residency in local markets is crucial. Dynatrace on Microsoft Azure allows enterprises to streamline deployment, gain critical insights, and automate manual processes. The result?

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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Optimising for High Latency Environments

CSS Wizardry

Last week, I posted a short update on LinkedIn about CrUX’s new RTT data. Chrome have recently begun adding Round-Trip-Time (RTT) data to the Chrome User Experience Report (CrUX). This gives fascinating insights into the network topography of our visitors, and how much we might be impacted by high latency regions. What is RTT?

Latency 244
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Optimizing Database Performance in Middleware Applications

DZone

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. Efficient database operations in middleware can dramatically improve overall system performance, reduce latency, and enhance user experience.

Database 222
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Why applying chaos engineering to data-intensive applications matters

Dynatrace

The jobs executing such workloads are usually required to operate indefinitely on unbounded streams of continuous data and exhibit heterogeneous modes of failure as they run over long periods. Performance is usually a primary concern when using stream processing frameworks. This significantly increases event latency.

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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Second, developers had to constantly re-learn new data modeling practices and common yet critical data access patterns. These include challenges with tail latency and idempotency, managing “wide” partitions with many rows, handling single large “fat” columns, and slow response pagination.

Latency 260