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Sustainability: Thoughts from a software engineer

Dynatrace

Platform engineers can set defaults for development teams, such as the number of replicas a service should have or whether it scales automatically. The post Sustainability: Thoughts from a software engineer appeared first on Dynatrace news. For instance, optimizing a frontend library can save resources for every website.

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Part 2: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Need to catch up? Check out Part 1.

Analytics 190
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The Role of Sanity Testing in Performance Engineering

DZone

As a CTO, my role extends beyond overseeing technical solutions I work closely with engineering teams to ensure performance testing strategies align with business goals. One of the recurring challenges Ive observed is how sanity testing, despite being a simple step, often determines the success or failure of performance testing efforts.

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O11y Guide: Finding Observability and DevEx Tranquility With Platform Engineering

DZone

By integrating observability as a first-class citizen within your platform engineering practices, you can simplify this challenge and stay on track in the ever-evolving cloud-native landscape.

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What Is Platform Engineering?

DZone

Platform engineering is the creation and management of foundational infrastructure and automated processes, incorporating principles like abstraction, automation, and self-service, to empower development teams, optimize resource utilization, ensure security, and foster collaboration for efficient and scalable software development.

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What is platform engineering?

Dynatrace

In response to this shift, platform engineering is growing in popularity. The practice of platform engineering has evolved alongside the increasing complexity of cloud environments. Platform engineers design and implement these platforms, as well as ensure their security, scalability, and reliability.

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Evolution of Recommendation Systems: From Legacy Rules Engines to Machine Learning

DZone

Historically, the first implementations of recommendation systems were built on legacy rule-based engines like IBM ODM (Operational Decision Manager) and Red Hat JBoss BRMS (Business Rule Management System). However, recent advances in machine learning have fundamentally changed how recommendations are generated.

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