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How Red Hat and Dynatrace intelligently automate your production environment

Dynatrace

This way, disruptions are minimized, MTTR is significantly decreased, and DevSecOps and SREs collaborate efficiently to boost productivity. 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.

DevOps 292
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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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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. ” Because of their versatility, teams can use IDPs for all types of software engineering projects, not just those in cloud-native scenarios.

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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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Conducting log analysis with an observability platform and full data context

Dynatrace

With more automated approaches to log monitoring and log analysis, however, organizations can gain visibility into their applications and infrastructure efficiently and with greater precision—even as cloud environments grow. Logs are automatically produced and time-stamped documentation of events relevant to cloud architectures.

Analytics 196
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Automated observability, security, and reliability at scale

Dynatrace

However, scaling up software development requires more tools along the software product lifecycle, which must be configured promptly and efficiently. This same mechanism can also be leveraged to validate the impact of new software releases on resources, logs, performance, reliability, or business measures.

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Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis

Dynatrace

AI data analysis can help development teams release software faster and at higher quality. AI-enabled chatbots can help service teams triage customer issues more efficiently. Check back here throughout the event for the latest news, insights, and announcements. How can organizations use AI observability to optimize AI costs?