GroundWork announced a beta program for the new GroundWork LogHub, which integrates one or more log analysis solutions to GroundWork Monitor Enterprise.
Combining unified monitoring with log analysis provides for faster trouble-shooting, improved root cause analysis, and more effective IT event correlation and forensic analysis.
GroundWork LogHub Beta 1 interfaces with the open source Elasticsearch ELK Stack (Elasticsearch, Kibana and Logstash). It allows data search, display and filtering functions to be displayed natively within the GroundWork Monitor portal UI for seamless visualization alongside other monitoring data.
Future releases of GroundWork LogHub are likely to include integrations with other popular log analysis tools, such as AppFirst, Sumo Logic and Splunk.
By integrating GroundWork Monitor Enterprise with log analysis tools, GroundWork LogHub provides:
- Bi-directional data flow between GroundWork Monitor and log analysis tools
- Correlated logfile-based data appears within the GroundWork Event Console for alerting, notification and actions
- Non-logfile data, such as performance or availability metrics, can be pushed to log analysis tools to improve search results
- Sending state changes, downtime schedules and other event data generated within GroundWork to the remote log analysis tool, improving the accuracy, precision and coverage of Log Analysis both for event correlation and forensic analysis
- Using a distributed log analysis tool for the collection and correlation of log data improves scalability and capability for monitoring Big Data systems, providing much improved correlation logic between log data and other monitoring events
The Latest
For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...
FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...
Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...
While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...
A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
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 ...