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Dynatrace Announces New Integration with Azure Spring Cloud

Dynatrace, in partnership with Microsoft, announced a new integration that provides full application data transparency into applications deployed on Azure Spring Cloud.

Azure Spring Cloud makes it easy to deploy Spring Boot-based microservice applications to Azure with zero code changes. Azure Spring Cloud manages your application infrastructure so that you can focus on application code and business logic. While Azure Spring Cloud excels at removing much of the labor associated with managing containerized workloads, the challenge of monitoring and maintaining the performance and health of these applications, or of troubleshooting issues when they occur, can be daunting—especially as organizations deploy these applications at massive scale.

Dynatrace removes the complexity of dynamic microservice workloads by providing automatic and intelligent observability, without requiring any code changes. This all happens out-of-the-box with the Dynatrace OneAgent, which automatically discovers and maps all applications, microservices, and infrastructure as well as any dependencies in dynamic, hybrid, and multicloud environments, without configuration or scripting, and without having to know which apps or cloud platforms are running. This provides end-to-end visibility into the running of the application, saving time spent manually identifying any anomalies and allowing teams to get to the root cause of code-level issues quicker. This means full transparency into application data and more time to focus on developing feature-rich applications for your end-users.

The Azure Spring Cloud and Dynatrace integration brings freedom to application developers, allowing them to manage instances by abstracting the underlying infrastructure. With Dynatrace ingesting metrics for Azure Spring Cloud, teams can see metrics for each service instance, split metrics into multiple dimensions, and create custom charts they can pin to their dashboards. By automatically delivering metrics for each instance, the Dynatrace Platform enables developers to focus where their effort matters most, on innovation.

“At Microsoft, we are committed to helping our customers modernize their applications and innovate faster than ever before,” Julia Liuson, Corporate VP, Developer Division, Microsoft. “By integrating a software intelligence solution like Dynatrace with Azure Spring Cloud, we can enable our customers with easy implementation of end-to-end observability, including automatic and continuous root-cause analysis, for their Spring Boot applications.”

“The ability to scale is critical for today’s digital business, as organizations have made the shift to cloud-native workloads and microservices,” said Eric Horsman, Global Director of Strategic Alliances at Dynatrace. “While cloud-native technologies and microservices have tremendous advantages, dynamic environments bring complexity that makes it difficult to understand the relationships and dependencies across an organization’s hybrid, multicloud ecosystem. Through the Dynatrace integration with Azure Spring Cloud, we are enabling full visibility into Spring Boot applications, which means more time innovating and a better product for end-users.”

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Dynatrace Announces New Integration with Azure Spring Cloud

Dynatrace, in partnership with Microsoft, announced a new integration that provides full application data transparency into applications deployed on Azure Spring Cloud.

Azure Spring Cloud makes it easy to deploy Spring Boot-based microservice applications to Azure with zero code changes. Azure Spring Cloud manages your application infrastructure so that you can focus on application code and business logic. While Azure Spring Cloud excels at removing much of the labor associated with managing containerized workloads, the challenge of monitoring and maintaining the performance and health of these applications, or of troubleshooting issues when they occur, can be daunting—especially as organizations deploy these applications at massive scale.

Dynatrace removes the complexity of dynamic microservice workloads by providing automatic and intelligent observability, without requiring any code changes. This all happens out-of-the-box with the Dynatrace OneAgent, which automatically discovers and maps all applications, microservices, and infrastructure as well as any dependencies in dynamic, hybrid, and multicloud environments, without configuration or scripting, and without having to know which apps or cloud platforms are running. This provides end-to-end visibility into the running of the application, saving time spent manually identifying any anomalies and allowing teams to get to the root cause of code-level issues quicker. This means full transparency into application data and more time to focus on developing feature-rich applications for your end-users.

The Azure Spring Cloud and Dynatrace integration brings freedom to application developers, allowing them to manage instances by abstracting the underlying infrastructure. With Dynatrace ingesting metrics for Azure Spring Cloud, teams can see metrics for each service instance, split metrics into multiple dimensions, and create custom charts they can pin to their dashboards. By automatically delivering metrics for each instance, the Dynatrace Platform enables developers to focus where their effort matters most, on innovation.

“At Microsoft, we are committed to helping our customers modernize their applications and innovate faster than ever before,” Julia Liuson, Corporate VP, Developer Division, Microsoft. “By integrating a software intelligence solution like Dynatrace with Azure Spring Cloud, we can enable our customers with easy implementation of end-to-end observability, including automatic and continuous root-cause analysis, for their Spring Boot applications.”

“The ability to scale is critical for today’s digital business, as organizations have made the shift to cloud-native workloads and microservices,” said Eric Horsman, Global Director of Strategic Alliances at Dynatrace. “While cloud-native technologies and microservices have tremendous advantages, dynamic environments bring complexity that makes it difficult to understand the relationships and dependencies across an organization’s hybrid, multicloud ecosystem. Through the Dynatrace integration with Azure Spring Cloud, we are enabling full visibility into Spring Boot applications, which means more time innovating and a better product for end-users.”

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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 ...