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The platform engineer role: A game-changer or just hype?

Dynatrace

Site reliability engineering first emerged to address cloud computing’s new performance needs. Today, the platform engineer role is gaining speed as the newest byproduct of scaling DevOps in the emerging but complex cloud-native world. Understanding the platform engineer role DevOps is a constantly evolving discipline.

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Platform engineering: Empowering key Kubernetes use cases with Dynatrace

Dynatrace

Today, speed and DevOps automation are critical to innovating faster, and platform engineering has emerged as an answer to some of the most significant challenges DevOps teams are facing. It needs to be engineered properly as a product or service, and it needs automation, observability, and security in itself.”

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Automating DevOps practices fuels speed and quality

Dynatrace

Still, while DevOps practices enable developer agility and speed as well as better code quality, they can also introduce complexity and data silos. More seamless handoffs between tasks in the toolchain can improve DevOps efficiency, software development innovation, and better code quality. They need automated DevOps practices.

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What is chaos engineering?

Dynatrace

Chaos engineering answers this need so organizations can deliver robust, resilient cloud-native applications that can stand up under any conditions. What is chaos engineering? Chaos engineers ask why. As chaos engineers grow confident in their testing, they change more variables and broaden the scope of the disaster.

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Site reliability engineering: 5 things you need to know

Dynatrace

What is site reliability engineering? Site reliability engineering (SRE) is the practice of applying software engineering principles to operations and infrastructure processes to help organizations create highly reliable and scalable software systems. Dynatrace news. SRE focuses on automation.

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How Dynatrace empowers performance engineering teams to test at scale

Dynatrace

But because of the complexity involved in executing and analyzing test results of dynamic systems, performance engineering is difficult to scale — especially with lean staff or resources. Grabner also introduced four ways organizations can turbocharge their performance engineering with automation. Automating root cause analysis.

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Data Engineers of Netflix?—?Interview with Samuel Setegne

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix.