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7 Trends in Docker Container Monitoring

Despite their benefits, containerized application environments have created exponential complexity in cloud-based application management and monitoring. Seven Trends in Docker Container Monitoring a study conducted by CA Technologies in collaboration with Gatepoint Research, identifies 7 key container challenges and trends.

Containers offer immense value to enterprises by allowing developers to easily build, ship and run any application virtually anywhere, as a lightweight, self-sufficient package. In turn, organizations benefit from the ability to enable instant application portability.

According to a recent Gartner study of 664 respondents, “65% of respondents stated that their organization expected to deploy containers into production by the end of 2017 ... Further, an additional 13% expect to deploy containers during 2018, suggesting that interest and adoption will only increase.” (Source: Gartner, Inc., Survey Analysis: Container Adoption and Deployment, 2018, Mark Warrilow, Dennis Smith, March 08, 2018, ID: G00348437)

"Digital transformation initiatives increasingly rely on cloud-based containerized applications that cannot be managed with traditional IT monitoring tools," said Ali Siddiqui, GM of the Agile Operations, CA Technologies. "These new app architectures create blind-spots or worse, a surge of false alarms that prevent teams from fixing problems quickly. CA Technologies unique approach to container monitoring delivers full visibility in these most dynamic environments."

Key container challenges and trends identified in the new survey include:

1. Docker popularity is rising

Docker popularity is rising, but more than 50 percent of organizations surveyed say their use of Docker container technology is "just getting started."

2. App development and testing is primary Docker use case

App development and testing is the primary Docker use case. Most executives surveyed said they use Docker container technology for application development and testing (61 percent) and to reduce their existing infrastructure (53 percent).

3. Docker provides the most value

Docker provides the most value when it comes to continuous delivery, scalability and resource efficiency. More than half of executives indicated that Docker containers provide continuation integration and delivery (58 percent), scalability (57 percent) and resource efficiency (53 percent).

4. Skills gap is biggest in Docker monitoring

The skills gap is biggest in Docker monitoring, according to respondents (48 percent).

Business impact of Docker container performance is unmeasured

The business impact of Docker container performance is largely unmeasured today. More than half of executives (56 percent) said they are not monitoring Docker container performance problems for business impact yet.

Visibility of container performance is biggest motivator

Complete visibility of container performance is the biggest motivator for organizations, according to 56 percent of executives.

Download the full report.

Container monitoring has become an essential application performance management tool for DevOps to rapidly develop and deploy innovative, cloud-based applications. However, as Docker and other container adoption continues to expand, so do the challenges around it.

Hot Topics

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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.

7 Trends in Docker Container Monitoring

Despite their benefits, containerized application environments have created exponential complexity in cloud-based application management and monitoring. Seven Trends in Docker Container Monitoring a study conducted by CA Technologies in collaboration with Gatepoint Research, identifies 7 key container challenges and trends.

Containers offer immense value to enterprises by allowing developers to easily build, ship and run any application virtually anywhere, as a lightweight, self-sufficient package. In turn, organizations benefit from the ability to enable instant application portability.

According to a recent Gartner study of 664 respondents, “65% of respondents stated that their organization expected to deploy containers into production by the end of 2017 ... Further, an additional 13% expect to deploy containers during 2018, suggesting that interest and adoption will only increase.” (Source: Gartner, Inc., Survey Analysis: Container Adoption and Deployment, 2018, Mark Warrilow, Dennis Smith, March 08, 2018, ID: G00348437)

"Digital transformation initiatives increasingly rely on cloud-based containerized applications that cannot be managed with traditional IT monitoring tools," said Ali Siddiqui, GM of the Agile Operations, CA Technologies. "These new app architectures create blind-spots or worse, a surge of false alarms that prevent teams from fixing problems quickly. CA Technologies unique approach to container monitoring delivers full visibility in these most dynamic environments."

Key container challenges and trends identified in the new survey include:

1. Docker popularity is rising

Docker popularity is rising, but more than 50 percent of organizations surveyed say their use of Docker container technology is "just getting started."

2. App development and testing is primary Docker use case

App development and testing is the primary Docker use case. Most executives surveyed said they use Docker container technology for application development and testing (61 percent) and to reduce their existing infrastructure (53 percent).

3. Docker provides the most value

Docker provides the most value when it comes to continuous delivery, scalability and resource efficiency. More than half of executives indicated that Docker containers provide continuation integration and delivery (58 percent), scalability (57 percent) and resource efficiency (53 percent).

4. Skills gap is biggest in Docker monitoring

The skills gap is biggest in Docker monitoring, according to respondents (48 percent).

Business impact of Docker container performance is unmeasured

The business impact of Docker container performance is largely unmeasured today. More than half of executives (56 percent) said they are not monitoring Docker container performance problems for business impact yet.

Visibility of container performance is biggest motivator

Complete visibility of container performance is the biggest motivator for organizations, according to 56 percent of executives.

Download the full report.

Container monitoring has become an essential application performance management tool for DevOps to rapidly develop and deploy innovative, cloud-based applications. However, as Docker and other container adoption continues to expand, so do the challenges around it.

Hot Topics

The Latest

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.