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What is platform engineering?

Dynatrace

Platform engineering is a practice that outlines how development teams build internal platforms to create self-service capabilities for software engineering teams. The result is a cloud-native approach to software delivery. Platform engineering cannot stand alone, however.

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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

Platform engineering creates and manages a shared infrastructure and set of tools, such as internal developer platforms (IDPs) , to enable software developers to build, deploy, and operate applications more efficiently. They shouldn’t worry about the platform; they should just start writing code.”

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Unlock the Power of DevSecOps with Newly Released Kubernetes Experience for Platform Engineering

Dynatrace

Platform engineering is on the rise. According to leading analyst firm Gartner, “80% of software engineering organizations will establish platform teams as internal providers of reusable services, components, and tools for application delivery…” by 2026. Monitoring-as-code can also be configured in GitOps fashion.

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Automated observability, security, and reliability at scale

Dynatrace

Dynatrace Configuration as Code enables complete automation of the Dynatrace platform’s configuration, ensuring that software is secure and reliable. With Configuration as Code, developers can manage their observability and security tasks with config files that can be developed alongside source code conveniently and at scale.

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Automating Success: Building a better developer experience with platform engineering

Dynatrace

At the 2024 Dynatrace Perform conference in Las Vegas, Michael Winkler, senior principal product management at Dynatrace, ran a technical session exploring just some of the many ways in which Dynatrace helps to automate the processes around development, releases, and operation. Configuration as code is easy to use, update, and understand.

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Nurturing Design in Your Software Engineering Culture

Strategic Tech

There are a few qualities that differentiate average from high performing software engineering organisations. I believe that attitude towards the design of code and architecture is one of them. Equally, code that accurately describes the domain it is modelling is often far easier to understand and change.

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Software engineering for machine learning: a case study

The Morning Paper

Software engineering for machine learning: a case study Amershi et al., Previously on The Morning Paper we’ve looked at the spread of machine learning through Facebook and Google and some of the lessons learned together with processes and tools to address the challenges arising. A general process. ICSE’19.