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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

However, this category requires near-immediate access to the current count at low latencies, all while keeping infrastructure costs to a minimum. Eventually Consistent : This category needs accurate and durable counts, and is willing to tolerate a slight delay in accuracy and a slightly higher infrastructure cost as a trade-off.

Latency 251
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How to overcome the cloud observability wall

Dynatrace

In contrast to modern software architecture, which uses distributed microservices, organizations historically structured their applications in a pattern known as “monolithic.” ” A monolithic software application has a few properties that are important to understand. Let’s break it down.

Cloud 246
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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

About two years ago, we, at our newly formed Machine Learning Infrastructure team started asking our data scientists a question: “What is the hardest thing for you as a data scientist at Netflix?” Our job as a Machine Learning Infrastructure team would therefore not be mainly about enabling new technical feats.

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Connect Fluentd logs with Dynatrace traces, metrics, and topology data to enhance Kubernetes observability

Dynatrace

Output plugins deliver logs to storage solutions, analytics tools, and observability platforms like Dynatrace. Detailed performance analysis for better software architecture and resource allocation. Processing plugins parse (normalize), filter, enrich (tagging), format, and buffer log streams.

Metrics 245
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Multi-tenants and Branches in Neon Serverless PostgreSQL

Percona

Tenants Multi-tenancy is a software architecture pattern where a single instance of a software application serves multiple tenants, allowing them to share resources like storage, processing power, and memory while maintaining separate, secure access to their respective data.

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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

About two years ago, we, at our newly formed Machine Learning Infrastructure team started asking our data scientists a question: “What is the hardest thing for you as a data scientist at Netflix?” Our job as a Machine Learning Infrastructure team would therefore not be mainly about enabling new technical feats.

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Cloud Adoption in 2020

O'Reilly

O’Reilly Learning > We wanted to discover what our readers were doing with cloud, microservices, and other critical infrastructure and operations technologies. Most (90%+) respondent organizations expect to increase their usage of cloud-based infrastructure. All told, we received 1,283 responses.

Cloud 144