Zenoss released streaming data monitoring for Kubernetes, the most widely deployed open-source orchestration platform used to manage containerized applications.
This real-time monitoring of Kubernetes streaming data is part of a broader set of initiatives focused on cloud-based monitoring, which enables visibility for ephemeral systems that cannot be effectively monitored by traditional monitoring tools.
Zenoss initially released monitoring and analytics for Kubernetes in 2018. Zenoss provides full-stack monitoring and AIOps for public and private clouds, as well as for all on-prem IT infrastructure. The platform provides a view of containerized applications in the context of the broader infrastructure. This provides a common view for IT Operations, DevOps, DevSecOps, and business-level users.
In addition to previously existing capabilities, Zenoss monitoring for Kubernetes now provides:
- Monitoring insights for Kubernetes clusters in a single pane of glass along with the broader infrastructure for K8s deployments in AWS, Azure and Google Cloud, as well as in private or hybrid clouds and locally hosted environments
- Secure, cloud-based monitoring with zero install
- Data collection and analytics in under five minutes
- Visibility into the health and performance of nodes, services, pods, containers, namespaces and more
- Intelligent dashboards with out-of-box templates
- Smart View, actions and notifications
"There is a significant shortage of visibility into health and performance in these highly complex container orchestration environments," said Trent Fitz, chief product officer for Zenoss. “Just as application developers have adapted to be more efficient, scalable and automated, we are doing the same for monitoring these environments.”
The Latest
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...
According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...
2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...
Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...