The real-user monitoring is mostly used to gauge the difference in performance for multitenant applications, This is so we can discern if there are any local network or client-facing issues when we do a comparison between each customer. It is quite important for us to be able to identify a client-side issue, as opposed to a feature managed problem, because we're essentially providing managed services of business applications.
Straightforward to integrate and automate.
Datadog's ability to group and visualize the servers and the data makes it relatively easy for the root cause analysis.
Provides monitoring more around business processes versus just servers, applications, etc. E.g., with complex systems, where a business process passes across multiple applications, the business needs us to monitor the heath of the process, not just a segment of the application.
The service maps that it creates, the health maps that it creates, the insights that it provides, etc., are all quite useful.
End-user Synthetics and monitoring are very good.
The event management part of TrueSight Operations Management, in my experience, is probably the best in the market. You have endless flexibility. You can build your own rules, you have the MRL language, and you can implement any kind of logic on the alerts. It may be correlation, abstraction, or executing something as a result of the alerts. You have almost the whole range of options available for event management using the available customization.
It's also easy to implement. The implementation of Geneos is very easy and interesting. It's not complicated. It's very quick to implement. The installation is very easy. There are many topics about ITRS Geneos that explain more about the features of the function of Geneos.
We could resolve issues that they had been facing for months or years. They had been having the same issues, the same performance problems, whether it was Excel taking a long time to load, or network instability, or voice call problems, and we would fix it in minutes, in front of them in a meeting, with absolute confidence. It would just blow their minds.
JVM memory monitoring and connection pool monitoring are valuable features.
The feature that I have found the most valuable is its user interface.
Advice From The CommunityRead answers to top Application Performance Management (APM) questions. 442,194 professionals have gotten help from our community of experts.
See more Application Performance Management (APM) questions »
Rony_SklarCommunity Manager at IT Central Station
With so many APM tools available, it can be hard for businesses to choose the right one for their needs. With this in mind, what is your favorite APM tool that you would happily recommend to others? What makes it your tool of choice?
Rony_SklarCommunity Manager at IT Central Station
How is synthetic monitoring used in Application Performance Management (APM)?
Menachem D PritzkerDirector of Growth at IT Central Station
Question: What do you think of the 2020 Gartner Magic Quadrant for Application Performance Monitoring?
Below are the rankings. What do you think? Gartner reports these four solutions as Leaders: Cisco (AppDynamics) Dynatrace New Relic Broadcom These are the Visionaries: Splunk (SignalFx) Datadog Only one Challenger: Microsoft Eight Niche Players: Riverbed (Aternity) IBM Instana Oracle SolarWinds Tingyun ManageEngine Micro Focus Thoughts?
What is Application Performance Management (APM)?
The best application performance monitoring solutions (APM) are important for proactively monitoring and managing a software application’s performance and availability. APM’s scope further includes performance measurement for virtually any IT asset that affects end user experience. The sign of the best APM tools are that they detect application performance issues in order to adhere to an agreed-upon service level. In particular, APM is focused on app response times under various load conditions. As part of this, APM also measures the compute resources required to support a given level of load.
According to members of the IT Central Station community, the best APM vendors serve multiple masters. Developers need to understand app performance characteristics in order to ensure an optimal software experience for end users. Business managers and IT department leaders use APM data to help make decisions about infrastructure and architecture.
As applications grow more complex and interdependent, application performance monitoring users express high expectations for potential APM toolsets. Accessibility, manageability and scalability are essential. Users argue that an effective APM tool must give business stakeholders accurate, understandable data while allowing developers to dive deeply into stored data over the long term.
DevOps users want app performance management tools to measure the deep internal transactions that take place inside an application or between integrated system elements. They want APM data in real time, across multiple application tiers, with transparency along the entire application process chain. Some refer to this as “full stack tracing.”
Ideally, APM data should be measured against user experience as a key performance indicator. For example, if a bottleneck is being caused by database latency, users want to understand the root cause so they can fix it immediately. This might require an alerting based on patterns and “baselining.”
Some expect APM tools to enable the discovery of complex distributed application architecture or even microservices and containers. After all, not all application architecture is known at the outset, and it certainly changes over time. Users need APM tools to be proactive whether they are used in dev, test, QA or production environments.
The APM toolset itself should have low impact on application performance. The measurements it takes have to be easy to interpret and place into a business-friendly reporting output. For instance, IT Central Station members suggest that APM tools should offer a predefined customizable reporting capability, with high visibility and a capacity to export and report on large quantities of raw data.