Dynatrace unveiled a new user experience for its Software Intelligence Platform, featuring powerful dashboarding capabilities and a visual interface to help drive tighter collaboration between development and business teams.
This UX powers Dynatrace Notebooks, a new interactive document capability that allows IT, development, security, and business teams to collaborate using code, text, and rich media to build, evaluate, and share insights from exploratory, causal-AI-based analytics projects.
These new capabilities add AI-powered graph analytics for custom queries to the powerful analytics that are already available out of the box with the Dynatrace® platform. This delivers instant and precise answers for an unlimited array of BizDevSecOps use cases. A few examples include:
- Protecting customers and brands by conducting application security forensics to identify, mitigate, and prevent data breaches.
- Improving customer satisfaction and helping to maximize revenue by querying for e-commerce customers who have not been able to finalize their check-out due to a service outage.
- Enabling more efficient multicloud operations by predicting cloud performance and utilization over time to optimize resource allocation based on user needs.
Dynatrace is also extending its platform’s Grail™ data lakehouse beyond logs and business events to deliver new support for metrics, distributed traces, and multicloud topology and dependencies.
This expands Grail’s ability to store, process, and analyze the enormous volume and variety of data from modern cloud ecosystems while retaining its context and without having to structure or rehydrate it.
Bernd Greifeneder, Founder and Chief Technology Officer at Dynatrace, said, “The ability to conduct exploratory, causal-AI-based analytics on petabytes of unified observability, security, and business event data multiplies the value of this data for our customers. Now, they can perform custom queries that leverage directed graphs that reflect continuously updated ecosystem topology and dependencies to derive answers with causation. These answers are far more precise than results from correlated data analytics and enable more powerful proactive and reactive analytics for expansive BizDevSecOps use cases and automation. The new Dynatrace user experience, which features collaborative dashboards and notebooks, is optimized for cross-team collaboration and interpreting and visualizing data in context. This allows people across organizations to make data-backed decisions and transforms the massive data from modern clouds into a goldmine for precise answers and intelligent automation.”
The new Dynatrace user experience, notebook capabilities, and Dynatrace Grail support for metrics and Dynatrace® Smartscape topology and dependency mapping will be available within 90 days of this announcement.
Grail support for Dynatrace® PurePath distributed traces will be available for early adopters within 90 days of this announcement.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.