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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.

DevOps 295
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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

” Because of their versatility, teams can use IDPs for all types of software engineering projects, not just those in cloud-native scenarios. Furthermore, OneAgent observes and gathers all remaining workload logs, metrics, traces, and events. “That means making it available, resilient, and secure,” Grabner said.

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SRE vs DevOps: What you need to know

Dynatrace

The events of 2020 accelerated the trend of organizations shifting to cloud-native technologies in response to the dramatic increase in demand for online services. Cloud-native environments bring speed and agility to software development and operations (DevOps) practices. Dynatrace news. SRE vs DevOps? Reduced latency. Efficiency.

DevOps 196
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Site reliability engineering: 5 things you need to know

Dynatrace

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. For more about this ongoing conversation, see A guide to event-driven SRE-inspired DevOps.

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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix. Pallavi, what’s your journey to data engineering at Netflix?

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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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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.