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How Red Hat and Dynatrace intelligently automate your production environment

Dynatrace

Problem remediation is too time-consuming According to the DevOps Automation Pulse Survey 2023 , on average, a software engineer takes nine hours to remediate a problem within a production application. Dynatrace Davis AI identifies the problem and maps the configuration change event to the root cause and the correct entity.

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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. Furthermore, OneAgent observes and gathers all remaining workload logs, metrics, traces, and events.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. This significantly increases event latency.

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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. Automation, automation, automation.

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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. As software development grows more complex, managing components using an automated onboarding process becomes increasingly important.

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Automating Success: Building a better developer experience with platform engineering

Dynatrace

The various presenters in this session aligned platform engineering use cases with the software development lifecycle. Check out the following use cases to learn how to drive innovation from development to production efficiently and securely with platform engineering observability. Real-time detection for fast remediation.

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Extend the AI and automation core of Dynatrace with host extensions to resolve infrastructure problems

Dynatrace

Let the Davis AI causation engine analyze additional metrics. Host extensions enable you to add additional metrics, events, and properties to hosts. All the data bound to hosts is analyzed by the Davis AI causation engine and made available on custom dashboards and events pages. What’s next.