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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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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.

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Comparing Approaches to Durability in Low Latency Messaging Queues

DZone

I have generally held the view that replicating data to a secondary system is faster than sync-ing to disk, assuming the round trip network delay wasn’t high due to quality networks and co-located redundant servers. Little’s Law and Why Latency Matters.

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For your eyes only: improving Netflix video quality with neural networks

The Netflix TechBlog

Recently, we added another powerful tool to our arsenal: neural networks for video downscaling. In this tech blog, we describe how we improved Netflix video quality with neural networks, the challenges we faced and what lies ahead. How can neural networks fit into Netflix video encoding?

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How to Scale Elasticsearch to Solve Your Scalability Issues

DZone

With the evolution of modern applications serving increasing needs for real-time data processing and retrieval, scalability does, too. 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.

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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.

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Dynatrace Managed turnkey Premium High Availability for globally distributed data centers (Early Adopter)

Dynatrace

The network latency between cluster nodes should be around 10 ms or less. With Dynatrace actively managing business-critical applications, some of our globally distributed enterprise customers require Dynatrace Managed to continue operating even when an entire data center goes down. Minimized cross-data center network traffic.