SL Corporation has expanded its coverage of Oracle technologies with the release of its Connector for Oracle Enterprise Manager. This new connector collects performance and diagnostics data directly from Oracle Enterprise Manager and enables application owners and support teams to view these metrics as part of an end-to-end view of application health and performance.
The Connector for Oracle Enterprise Manager rounds out the existing real-time monitoring capabilities of SL’s Solution Packages for Oracle Coherence, Oracle WebLogic and Oracle Database, which form the core technology platform for many mission-critical applications or enterprise Platform as a Service (PaaS).
As these critical applications become more complex, gaining real-time visibility and insight becomes both more critical and more difficult. When application downtime means a significant loss of revenue, it is imperative to have a monitoring solution in place that can instantly assess application health and performance across all tiers and vendors.
“Our investment in the Oracle platform greatly enhances our customer’s ability to monitor their most important and valuable custom applications” said Ted Wilson, COO at SL. “When coupled with our native monitoring of TIBCO, IBM, VMware and even open source technologies like Tomcat and MySQL, our deep support for Oracle products delivers true end-to-end visibility.”
SL’s RTView Enterprise Monitor product is an easy to use, easy to configure application monitor that uses pluggable solution packages to monitor multi-tier, multi-vendor application environments. Using pre-built dashboards and pre-defined alerts, RTView Enterprise Monitor is ideal for application support teams, developers and architects that need to maintain platform service levels and monitor resource utilization to support growing enterprise demands.
The Connector for Oracle Enterprise Manager simplifies data collection from your Oracle database and WebLogic servers by utilizing Oracle Enterprise Manager (OEM) diagnostic data to collect health and performance metrics. OEM is commonly used to manage Oracle technology at the component level and doesn’t always provide visibility across components. RTView Enterprise Monitor augments OEM’s data collection capability with dynamic correlation across tiers and vendors, historical analytics and advanced visualization dashboards.
The Solution Package for Oracle Coherence provides real-time performance analysis of Oracle Coherence environments at the node, cluster and application level. It monitors workload distribution, historical performance trends, resource usage, hotspots and bottlenecks while providing real-time alerts beyond the capabilities of OEM.
The Solution Package for Oracle WebLogic provides powerful, intuitive operational intelligence for large scale WebLogic environments at the server, cluster and application levels. Users can quickly diagnose complex issues like cluster load balancing or performance anomalies and then drill down to the exact server, JVM, connection or thread pool. WebLogic server performance can also be correlated with hosts, virtual machines, networks, databases or message queues.
The Solution Package for Oracle Database collects health and performance information at near-time intervals that enables you to diagnose transient problems through the use of historical trends graphs. This tool is particularly suited to application support teams that don’t have deep DBA skills but rather need to diagnose database problems in the context of the overall application.
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 ...