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Compuware APM Offers Hadoop Performance Optimization

Compuware Corporation announced pricing for its deep transaction management solution for optimizing performance of Apache Hadoop applications - Compuware APM’s dynaTrace Enterprise for Hadoop will be offered starting at just $1,000 per Hadoop Java Virtual Machine (JVM).

Applications using Hadoop MapReduce suffer from many of the performance challenges and bottlenecks that plague current distributed applications, though the volume of performance data can be much larger. Optimizing compute and data distribution across nodes, assuring job execution efficiency, identifying I/O bottlenecks, tuning CPU and memory consumption amongst thousands of nodes requires deep insight into the Hadoop environment.

With its patented PurePath Technology, Compuware dynaTrace Enterprise provides visibility into Hadoop applications and supports these highly scalable, elastic environments in several unique ways:

- Zero-Configuration Instrumentation: Out-of-the-box dashboards for 100 percent deep visibility into Hadoop MapReduce performance, with no code changes required and easy to deploy and manage.

- One-Click Hotspot Analysis: Faster mean-time-to-resolve (MTTR) with one-click hotspot analysis of MapReduce jobs, including long-running and highly distributed jobs. See root cause in minutes instead of hours or days.

- Automated Performance Analytics: Optimize Hadoop environments and save costs with deep insight into how MapReduce jobs consume resources, scale across cluster and automated performance analytics from the task-level down to individual method execution times.

- Correlated Cluster Health Monitoring: Monitor Hadoop cluster overall and down to individual machines as well as monitor CPU, memory, disk, I/O and garbage collection to detect and correlate system health to job performance. Proactively fix issues before they impact SLAs.

- Automatic MapReduce Error Correlation With Job, Task and Method level Detail: For faster MTTR than any other approach in the market.

“The market for Hadoop applications is exploding,” said John Van Siclen, General Manager of Compuware’s APM business unit. “Our Hadoop customers seek a new generation APM approach that goes beyond log-file analysis and point tools. They expect an APM system that’s built for the Hadoop architecture, supports dynamic, elastic environments out-of-the-box, and is easy to deploy and use. Additionally, customers need this APM system to be affordable for the highly scalable Hadoop environments they run. We believe dynaTrace Enterprise, coupled with our pricing model, will accelerate the growth of the Hadoop market – making highly optimized, highly tuned Hadoop implementations much easier to achieve.”

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Compuware APM Offers Hadoop Performance Optimization

Compuware Corporation announced pricing for its deep transaction management solution for optimizing performance of Apache Hadoop applications - Compuware APM’s dynaTrace Enterprise for Hadoop will be offered starting at just $1,000 per Hadoop Java Virtual Machine (JVM).

Applications using Hadoop MapReduce suffer from many of the performance challenges and bottlenecks that plague current distributed applications, though the volume of performance data can be much larger. Optimizing compute and data distribution across nodes, assuring job execution efficiency, identifying I/O bottlenecks, tuning CPU and memory consumption amongst thousands of nodes requires deep insight into the Hadoop environment.

With its patented PurePath Technology, Compuware dynaTrace Enterprise provides visibility into Hadoop applications and supports these highly scalable, elastic environments in several unique ways:

- Zero-Configuration Instrumentation: Out-of-the-box dashboards for 100 percent deep visibility into Hadoop MapReduce performance, with no code changes required and easy to deploy and manage.

- One-Click Hotspot Analysis: Faster mean-time-to-resolve (MTTR) with one-click hotspot analysis of MapReduce jobs, including long-running and highly distributed jobs. See root cause in minutes instead of hours or days.

- Automated Performance Analytics: Optimize Hadoop environments and save costs with deep insight into how MapReduce jobs consume resources, scale across cluster and automated performance analytics from the task-level down to individual method execution times.

- Correlated Cluster Health Monitoring: Monitor Hadoop cluster overall and down to individual machines as well as monitor CPU, memory, disk, I/O and garbage collection to detect and correlate system health to job performance. Proactively fix issues before they impact SLAs.

- Automatic MapReduce Error Correlation With Job, Task and Method level Detail: For faster MTTR than any other approach in the market.

“The market for Hadoop applications is exploding,” said John Van Siclen, General Manager of Compuware’s APM business unit. “Our Hadoop customers seek a new generation APM approach that goes beyond log-file analysis and point tools. They expect an APM system that’s built for the Hadoop architecture, supports dynamic, elastic environments out-of-the-box, and is easy to deploy and use. Additionally, customers need this APM system to be affordable for the highly scalable Hadoop environments they run. We believe dynaTrace Enterprise, coupled with our pricing model, will accelerate the growth of the Hadoop market – making highly optimized, highly tuned Hadoop implementations much easier to achieve.”

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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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...