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ManageEngine Expands Big Data Monitoring to Hadoop

ManageEngine announced the availability of performance monitoring for new big data platforms, such as Hadoop and Oracle Coherence, in Applications Manager, its application performance monitoring solution.

The move enables IT operations teams in enterprises to gain operational intelligence into big data platforms such as Hadoop and Oracle Coherence as well as the business-critical applications relying on these platforms.

"Monitoring big data systems goes beyond the traditional APM approach and requires a deeper understanding of the entire SaaS stack," said Dev Anand, director of product management at ManageEngine. "Backed by our experience with Zoho's online services, we were able to tune Applications Manager to provide insight at the application, database and file levels."

Hadoop is an open source framework for distributed storing and processing of big data on large clusters of commodity hardware. Applications Manager enables comprehensive performance monitoring of Hadoop clusters to minimize downtime and performance degradation as well as take corrective action proactively before any problems arise. The key performance indicators of Hadoop monitored by Applications Manager include those pertaining to the Hadoop Distributed File System (HDFS), TaskTrackers/NodeManagers, jobs/applications, files and directories, and blocks.

Oracle Coherence is an in-memory grid and distributed caching solution that enables enterprises to scale mission-critical applications. Applications Manager's monitoring support for Oracle Coherence provides deep insights into the health and performance of Coherence clusters and facilitates rapid troubleshooting of issues. The key performance metrics of Oracle Coherence monitored by Applications Manager include those related to clusters, partition assignment, distributed and replicated services, Extend Connection, Extend Services, and distributed and replicated node memory details.

Among its many benefits, Hadoop and Oracle Coherence monitoring in Applications Manager helps IT personnel:

- Gain a 360-degree view into the performance of Hadoop and Oracle Coherence clusters, the applications that rely on them, and the associated infrastructure components.

- Get proactive alerts on a wide array of error conditions and faults. Diagnose and repair performance issues faster and keep critical applications up and running.

- Monitor resource utilization to ensure critical workloads do not run out of resources. Make informed decisions on capacity planning to handle the increasing size and complexity of applications.

- Assess the value delivered by big data processes to the enterprise.

The support for Hadoop and Oracle Coherence is complementary to the existing out-of-the-box support for 80+ applications and infrastructure components, including other big data/NoSQL technologies such as Cassandra, MongoDB, Redis, Memcached and Couchbase.

Applications Manager 12.7 is available immediately.

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ManageEngine Expands Big Data Monitoring to Hadoop

ManageEngine announced the availability of performance monitoring for new big data platforms, such as Hadoop and Oracle Coherence, in Applications Manager, its application performance monitoring solution.

The move enables IT operations teams in enterprises to gain operational intelligence into big data platforms such as Hadoop and Oracle Coherence as well as the business-critical applications relying on these platforms.

"Monitoring big data systems goes beyond the traditional APM approach and requires a deeper understanding of the entire SaaS stack," said Dev Anand, director of product management at ManageEngine. "Backed by our experience with Zoho's online services, we were able to tune Applications Manager to provide insight at the application, database and file levels."

Hadoop is an open source framework for distributed storing and processing of big data on large clusters of commodity hardware. Applications Manager enables comprehensive performance monitoring of Hadoop clusters to minimize downtime and performance degradation as well as take corrective action proactively before any problems arise. The key performance indicators of Hadoop monitored by Applications Manager include those pertaining to the Hadoop Distributed File System (HDFS), TaskTrackers/NodeManagers, jobs/applications, files and directories, and blocks.

Oracle Coherence is an in-memory grid and distributed caching solution that enables enterprises to scale mission-critical applications. Applications Manager's monitoring support for Oracle Coherence provides deep insights into the health and performance of Coherence clusters and facilitates rapid troubleshooting of issues. The key performance metrics of Oracle Coherence monitored by Applications Manager include those related to clusters, partition assignment, distributed and replicated services, Extend Connection, Extend Services, and distributed and replicated node memory details.

Among its many benefits, Hadoop and Oracle Coherence monitoring in Applications Manager helps IT personnel:

- Gain a 360-degree view into the performance of Hadoop and Oracle Coherence clusters, the applications that rely on them, and the associated infrastructure components.

- Get proactive alerts on a wide array of error conditions and faults. Diagnose and repair performance issues faster and keep critical applications up and running.

- Monitor resource utilization to ensure critical workloads do not run out of resources. Make informed decisions on capacity planning to handle the increasing size and complexity of applications.

- Assess the value delivered by big data processes to the enterprise.

The support for Hadoop and Oracle Coherence is complementary to the existing out-of-the-box support for 80+ applications and infrastructure components, including other big data/NoSQL technologies such as Cassandra, MongoDB, Redis, Memcached and Couchbase.

Applications Manager 12.7 is available immediately.

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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.