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Dynatrace Adds New Capabilities

Dynatrace announced product improvements including improved user experience, advanced log insights, and extended cloud-native capabilities, equipping businesses with essential technology to boost operational efficiency and drive productivity.

Key Advancements Include:

- Improved User Experience: With the newest advancements, users have easier access to the insights they need to visualize and dive deeper into their data, analyze it in context, and drive proactive, AI-powered automation. Simplified dashboards, streamlined navigation, and consistent interfaces seamlessly extend the power of Dynatrace across every team and department. Onboarding is frictionless, with in-product guidance facilitating smoother cross-functional communication.

- Next-Level Log Management and Analytics: Operations, SREs, DevOps, Cloud, and Security teams can now access log insights tailored to their needs, without manual effort, and without compromising security and privacy. With these advanced logs solutions, teams can derive greater value from logs faster and at scale with the ability to automatically ingest, manage, and analyze logs without complex manual setup. This is supported by Dynatrace OpenPipeline™, which enables teams to process data from various sources in virtually any format, and Dynatrace Grail™, a data lakehouse designed explicitly for observability and security data.

- Extended Capabilities for Cloud Native Teams: Accessing deeper observability and insights into cloud workloads is now simpler for cloud-native operations, SREs, and platform engineering teams. Extended AI-powered analytics and workflows with additional hyperscaler integrations enable streamlined cloud operations that help teams take full advantage of the agility and scalability of cloud environments. Teams can effortlessly manage across multiple cloud providers with innovations like Kubernetes Health Management and the Dynatrace Clouds™ app, while also benefiting from automatic security assessments with solutions like Dynatrace Kubernetes Security Posture Management (KSPM).

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Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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Dynatrace Adds New Capabilities

Dynatrace announced product improvements including improved user experience, advanced log insights, and extended cloud-native capabilities, equipping businesses with essential technology to boost operational efficiency and drive productivity.

Key Advancements Include:

- Improved User Experience: With the newest advancements, users have easier access to the insights they need to visualize and dive deeper into their data, analyze it in context, and drive proactive, AI-powered automation. Simplified dashboards, streamlined navigation, and consistent interfaces seamlessly extend the power of Dynatrace across every team and department. Onboarding is frictionless, with in-product guidance facilitating smoother cross-functional communication.

- Next-Level Log Management and Analytics: Operations, SREs, DevOps, Cloud, and Security teams can now access log insights tailored to their needs, without manual effort, and without compromising security and privacy. With these advanced logs solutions, teams can derive greater value from logs faster and at scale with the ability to automatically ingest, manage, and analyze logs without complex manual setup. This is supported by Dynatrace OpenPipeline™, which enables teams to process data from various sources in virtually any format, and Dynatrace Grail™, a data lakehouse designed explicitly for observability and security data.

- Extended Capabilities for Cloud Native Teams: Accessing deeper observability and insights into cloud workloads is now simpler for cloud-native operations, SREs, and platform engineering teams. Extended AI-powered analytics and workflows with additional hyperscaler integrations enable streamlined cloud operations that help teams take full advantage of the agility and scalability of cloud environments. Teams can effortlessly manage across multiple cloud providers with innovations like Kubernetes Health Management and the Dynatrace Clouds™ app, while also benefiting from automatic security assessments with solutions like Dynatrace Kubernetes Security Posture Management (KSPM).

The Latest

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...