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Nagios Unveils Log Management Solution

Nagios Enterprises unveiled Nagios Log Server, a \log monitoring and management solution that allows organizations to quickly and easily view, sort, and configure logs from any source on a given network.

Log Server extends Nagios' existing network management offerings by providing users with the ability to dive deep into network events, logs, and performance standards.

Nagios Log Server greatly simplifies the process of managing network log data. With configuration wizards, users can get up and running quickly to start monitoring their logs in minutes. Log Server provides a central dashboard and management interface to easily oversee and drill down into infrastructure issues, network errors, and log events.

Log Server also can scale to meet the needs of any organization, so as an organization grows, additional instances can easily be incorporated into an existing server cluster - allowing for more power, speed, storage, and reliability to be added to the monitoring system. Automatic high-availability and fail-over capabilities are built into the back-end of Log Server to ensure data retention and storage security.

Designed for security and network auditing, Log Server provides users with powerful query dashboards, an alerting and notification engine, and a fully accessible API. Custom alert thresholds as well as query rules enable system administrators to mitigate compromises, and resolve security vulnerabilities before they affect critical business processes. Log Server can adapt to existing environments with back-end API access and a number of notification methods.

Common implementations of Log Server include data retention and network auditing, security investigation, root cause analysis, system diagnostics, log event data backups and storage, change control and auditing, debugging and troubleshooting, and the identification of performance bottlenecks.

Additional key features include comprehensive analysis dashboards, pre-packaged high-availability and fail-over capabilities, built-in and integrated alerting options, highly scalable architecture, an extendable API, product integration, and more.

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Nagios Unveils Log Management Solution

Nagios Enterprises unveiled Nagios Log Server, a \log monitoring and management solution that allows organizations to quickly and easily view, sort, and configure logs from any source on a given network.

Log Server extends Nagios' existing network management offerings by providing users with the ability to dive deep into network events, logs, and performance standards.

Nagios Log Server greatly simplifies the process of managing network log data. With configuration wizards, users can get up and running quickly to start monitoring their logs in minutes. Log Server provides a central dashboard and management interface to easily oversee and drill down into infrastructure issues, network errors, and log events.

Log Server also can scale to meet the needs of any organization, so as an organization grows, additional instances can easily be incorporated into an existing server cluster - allowing for more power, speed, storage, and reliability to be added to the monitoring system. Automatic high-availability and fail-over capabilities are built into the back-end of Log Server to ensure data retention and storage security.

Designed for security and network auditing, Log Server provides users with powerful query dashboards, an alerting and notification engine, and a fully accessible API. Custom alert thresholds as well as query rules enable system administrators to mitigate compromises, and resolve security vulnerabilities before they affect critical business processes. Log Server can adapt to existing environments with back-end API access and a number of notification methods.

Common implementations of Log Server include data retention and network auditing, security investigation, root cause analysis, system diagnostics, log event data backups and storage, change control and auditing, debugging and troubleshooting, and the identification of performance bottlenecks.

Additional key features include comprehensive analysis dashboards, pre-packaged high-availability and fail-over capabilities, built-in and integrated alerting options, highly scalable architecture, an extendable API, product integration, and more.

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 ...