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Compuware APM Unveils Solution to Optimize Hadoop on AWS

Compuware Corporation has released an APM solution for Hadoop on Amazon Elastic MapReduce, (EMR) available now in the AWS Marketplace.

This solution enables organizations to tame big data at scale, enabling customers to gain faster business value at lower cost, and furthers Compuware APM's ability to provide visibility and troubleshooting insight into big data workloads.

Powered by its patented PurePath Technology, Compuware APM profiles Amazon EMR jobs, providing drill-down dashboards that can pinpoint the root cause of failed jobs or performance hotspots with a single click. Operation teams gain full visibility into cluster usage based on users or job types, enabling monitoring of service level agreements (SLAs) and charge-back models to consumers.

By profiling Hadoop jobs in production, operations teams can quickly identify the issues, whether they are misconfigured or unbalanced clusters, poorly-coded workloads or unhealthy hosts. Developers, armed with exact detail shared by operations and QA, no longer have to guess at the performance of their code when running at massive scale.

"Compuware APM combined with the AWS cloud provides customers with the technical capabilities they need to allow them to focus on their business," said Terry Hanold, VP, Cloud Commerce, AWS. "We're excited to welcome Compuware into the AWS Marketplace, where customers will be able to now gain the advantage of Compuware APM for Amazon EMR."

Features and capabilities of Compuware APM solution with Amazon EMR include:

- Profiling Hadoop jobs in production clusters to see which teams are utilizing the cluster and exactly why, to code level, a job takes minutes or hours to run.

- Automatically identifying performance hotspots in Amazon EMR, such as whether the problem is due to a poor configuration, over-utilized or failed infrastructure, or inefficient code.

- Eliminating the need to scour log files by identifying the root cause of a job failure in one click. Exceptions, stack traces and logged data are automatically identified by PurePath Technology, showing both where and why failures occur. Deep facts can be easily shared with development to quickly fix issues.

"Compuware APM makes application performance management for big data both simple and straightforward across the lifecycle from development to test to production," said Steve Tack, VP of Product Management for Compuware's APM business unit. "To help customers grappling with this maturing technology where expertise is scarce, Compuware APM for Amazon EMR adds visibility and helps organizations better manage their big data workloads and transactions, enabling them to leap ahead in the analytics race."

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Compuware APM Unveils Solution to Optimize Hadoop on AWS

Compuware Corporation has released an APM solution for Hadoop on Amazon Elastic MapReduce, (EMR) available now in the AWS Marketplace.

This solution enables organizations to tame big data at scale, enabling customers to gain faster business value at lower cost, and furthers Compuware APM's ability to provide visibility and troubleshooting insight into big data workloads.

Powered by its patented PurePath Technology, Compuware APM profiles Amazon EMR jobs, providing drill-down dashboards that can pinpoint the root cause of failed jobs or performance hotspots with a single click. Operation teams gain full visibility into cluster usage based on users or job types, enabling monitoring of service level agreements (SLAs) and charge-back models to consumers.

By profiling Hadoop jobs in production, operations teams can quickly identify the issues, whether they are misconfigured or unbalanced clusters, poorly-coded workloads or unhealthy hosts. Developers, armed with exact detail shared by operations and QA, no longer have to guess at the performance of their code when running at massive scale.

"Compuware APM combined with the AWS cloud provides customers with the technical capabilities they need to allow them to focus on their business," said Terry Hanold, VP, Cloud Commerce, AWS. "We're excited to welcome Compuware into the AWS Marketplace, where customers will be able to now gain the advantage of Compuware APM for Amazon EMR."

Features and capabilities of Compuware APM solution with Amazon EMR include:

- Profiling Hadoop jobs in production clusters to see which teams are utilizing the cluster and exactly why, to code level, a job takes minutes or hours to run.

- Automatically identifying performance hotspots in Amazon EMR, such as whether the problem is due to a poor configuration, over-utilized or failed infrastructure, or inefficient code.

- Eliminating the need to scour log files by identifying the root cause of a job failure in one click. Exceptions, stack traces and logged data are automatically identified by PurePath Technology, showing both where and why failures occur. Deep facts can be easily shared with development to quickly fix issues.

"Compuware APM makes application performance management for big data both simple and straightforward across the lifecycle from development to test to production," said Steve Tack, VP of Product Management for Compuware's APM business unit. "To help customers grappling with this maturing technology where expertise is scarce, Compuware APM for Amazon EMR adds visibility and helps organizations better manage their big data workloads and transactions, enabling them to leap ahead in the analytics race."

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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 ...

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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 ...