Remove 2007 Remove Design Remove Efficiency Remove Processing
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DevOps automation: From event-driven automation to answer-driven automation [with causal AI]

Dynatrace

In the world of DevOps and SRE, DevOps automation answers the undeniable need for efficiency and scalability. This evolution in automation, referred to as answer-driven automation, empowers teams to address complex issues in real time, optimize workflows, and enhance overall operational efficiency.

DevOps 217
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Transparent Huge Pages Refresher

Percona

The concept of HugePages in Linux has existed for many years, first introduced in 2007. However, the inclusion of the HugePages feature allows the Linux kernel to efficiently handle substantial memory pages alongside the standard 4KB size.

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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. The Netflix video processing pipeline went live with the launch of our streaming service in 2007. The Netflix video processing pipeline went live with the launch of our streaming service in 2007.

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Should You Use ClickHouse as a Main Operational Database?

Percona

However, ClickHouse is super efficient for timeseries and provides “sharding” out of the box (scalability beyond one node). Although such databases can be very efficient with counts and averages, some queries will be slow or simply non existent. Inserts are efficient for bulk inserts only. Processed 4.15

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Progress Delayed Is Progress Denied

Alex Russell

Apple's policy against browser engine choice adds years of delays beyond the (expected) delay of design iteration, specification authoring, and browser feature development. Efficiently enables new styles of drawing content on the web , removing many hard tradeoffs between visual richness , accessibility, and performance.

Media 145
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Data Mining Problems in Retail

Highly Scalable

More specialized data mining applications like supply chain optimization and fraud detection are out of scope, as well as the implementation details of the data mining process (such as evaluation of model quality). The design of the model heavily depends on the problem. This model helps to design targeted usage expansion campaigns.

Retail 152
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Scaling Media Machine Learning at Netflix

The Netflix TechBlog

We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. We accomplish this by paving the path to: Accessing and processing media data (e.g. An average title has 2k shots, which means that we need to enumerate and process ~2M pairs. mp4, clip1.mp4,

Media 290