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Dynatrace Introduces AppEngine

Dynatrace announced the launch of the AppEngine.

This new Dynatrace platform technology empowers customers and partners with an easy-to-use, low-code approach to create custom, compliant, and intelligent data-driven apps for their IT, development, security, and business teams. These custom apps can address boundless BizDevSecOps use cases and unlock the wealth of insights available in the explosive amounts of data generated by modern cloud ecosystems.

To demonstrate the power and flexibility of the Dynatrace AppEngine, the company also unveiled a range of new apps that address a variety of use cases. These will be available to Dynatrace customers and include the following:

- Smartscape® Health View enables teams to visualize their applications’ vital signs, including security posture. It also showcases AppEngine’s ability to unlock actionable insights from data through enrichment, visualization, and analytics.
- Site Reliability Guardian helps teams proactively maintain service level objectives (SLOs) by automating quality and security gates. It also exemplifies how apps created using the AppEngine fuel answer-driven automation to optimize cloud operations.

- Carbon Impact enables teams to understand and reduce the carbon footprint of their hybrid and multicloud ecosystems. It also demonstrates how AppEngine can help teams measure and optimize the key performance indicators (KPIs) that matter most for business executives or regulatory requirements.

The Dynatrace platform consolidates observability, security, and business data with full context and dependency mapping. This frees customers from manual approaches such as tagging to connect siloed data, using imprecise machine-learning analytics, and the high operational costs of other solutions. AppEngine leverages this data and simplifies intelligent app creation and integrations for teams throughout an organization. It provides automatic scalability, runtime application security, safe connections and integrations across hybrid and multicloud ecosystems, and full lifecycle support, including security and quality certifications. As a result, for the first time, any team in an organization can leverage causal AI to create intelligent apps and integrations for use cases and technologies specific to their unique business requirements and technology stacks.

“The Dynatrace platform has always helped IT, development, business, and security teams succeed by delivering precise answers and intelligent automation across their complex and dynamic cloud ecosystems,” said Bernd Greifeneder, Founder and Chief Technology Officer at Dynatrace. “Now, with the Dynatrace AppEngine, it’s easy to create apps that leverage vast observability, security, and business data from modern clouds and Dynatrace’s causal AI. This extends precise answers and intelligent automation to boundless BizDevSecOps use cases, empowering more people across organizations to make data-backed decisions.”

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Dynatrace Introduces AppEngine

Dynatrace announced the launch of the AppEngine.

This new Dynatrace platform technology empowers customers and partners with an easy-to-use, low-code approach to create custom, compliant, and intelligent data-driven apps for their IT, development, security, and business teams. These custom apps can address boundless BizDevSecOps use cases and unlock the wealth of insights available in the explosive amounts of data generated by modern cloud ecosystems.

To demonstrate the power and flexibility of the Dynatrace AppEngine, the company also unveiled a range of new apps that address a variety of use cases. These will be available to Dynatrace customers and include the following:

- Smartscape® Health View enables teams to visualize their applications’ vital signs, including security posture. It also showcases AppEngine’s ability to unlock actionable insights from data through enrichment, visualization, and analytics.
- Site Reliability Guardian helps teams proactively maintain service level objectives (SLOs) by automating quality and security gates. It also exemplifies how apps created using the AppEngine fuel answer-driven automation to optimize cloud operations.

- Carbon Impact enables teams to understand and reduce the carbon footprint of their hybrid and multicloud ecosystems. It also demonstrates how AppEngine can help teams measure and optimize the key performance indicators (KPIs) that matter most for business executives or regulatory requirements.

The Dynatrace platform consolidates observability, security, and business data with full context and dependency mapping. This frees customers from manual approaches such as tagging to connect siloed data, using imprecise machine-learning analytics, and the high operational costs of other solutions. AppEngine leverages this data and simplifies intelligent app creation and integrations for teams throughout an organization. It provides automatic scalability, runtime application security, safe connections and integrations across hybrid and multicloud ecosystems, and full lifecycle support, including security and quality certifications. As a result, for the first time, any team in an organization can leverage causal AI to create intelligent apps and integrations for use cases and technologies specific to their unique business requirements and technology stacks.

“The Dynatrace platform has always helped IT, development, business, and security teams succeed by delivering precise answers and intelligent automation across their complex and dynamic cloud ecosystems,” said Bernd Greifeneder, Founder and Chief Technology Officer at Dynatrace. “Now, with the Dynatrace AppEngine, it’s easy to create apps that leverage vast observability, security, and business data from modern clouds and Dynatrace’s causal AI. This extends precise answers and intelligent automation to boundless BizDevSecOps use cases, empowering more people across organizations to make data-backed decisions.”

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...