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Incident management is streamlined with Moogsoft. One standout feature is its unique situation-creation capability, differentiating it from other fault management tools. While other tools typically convert alarms directly into tickets or incidents, Moogsoft adds a middle layer where multiple alarms can be aggregated into one incident. Moogsoft's strong AI capabilities also allow it to correlate similar alarms automatically based on past experiences.

The solution comes with a collaboration feature. This feature allows team members to communicate and work together on various issues. If there are ten incidents, then ten chats will be open.

The solution comes with features like PRC, designed to streamline alarm flow, reduce noise, and prevent alarm flooding. These features are particularly beneficial when used with a basic understanding of network operations. It has a time-based algorithm that continuously learns from alarm flows. When the alarm flow reaches a certain limit, it collects all alarms together based on historical data. This is particularly useful during major events such as power outages, where alarms could flood. In such cases, the product activates to hold all alarms for a certain period, anticipating a fluctuation rather than immediately triggering the alert manager.

The tool needs to improve its support. It appears that the support responsiveness from Moogsoft is not aligned with the severity of the incident. Instead of proactively addressing issues, customers have to chase Moogsoft for resolution. In Moogsoft, unlike other tools like Splunk, the process follows a step-by-step sequence. You need to start each process in a specific order, typically following a sequence. 

I have been working with the solution for five years. 

The tool is scalable. About 70 customers were able to run on the tool's single instance. My company has customers spread across various industries, including FMCG, and  hospitality. 

I have used Netcool and Splunk before, and each tool has its own capabilities, advantages, and disadvantages. Splunk is great for heavy data processing, while Moogsoft excels in integrating with various tools through intelligent plugins. With Moogsoft, you install the plugin and provide basic login credentials for integration. Every tool has its unique strengths and experiences. There's no situation where Moogsoft can do something that other tools can't, or vice versa. For example, Moogsoft offers intelligent alarm correlation through situations, while Netcool provides impact policies for coordinating different alarms.

Installing Moogsoft requires moderate effort, as it's not overly complex but does demand some expertise. Users should understand databases and Linux file systems well to ensure a smooth installation process. While it's not overly complicated, having an experienced individual handle the installation is recommended. 

The tool's maintenance is smooth and easy. 

I rate the product an eight out of ten. 

My main use cases with Dynatrace include a plethora of functionalities. Real user monitoring (RUM) is being used extensively. Synthetic monitoring is used extensively. We use Kafka monitoring. I have awareness of how AWS metrics are being sent, whether it's a direct integration or only account-level integration. We use it significantly.

Some of the best features I appreciate about Dynatrace include synthetic monitoring, where you can get into HTTP level monitoring and click browser monitoring, which is available in both Splunk and Dynatrace. This is very useful in our environment. Apart from that, mobile-based monitoring, which we have embedded in some cases with the apps that are connected, is also beneficial for monitoring APIs.

Synthetic monitoring has had a significant impact on my ability to track performance proactively. It has been very useful. It serves two aspects: synthetic monitoring is primarily for the front-end side where availability and tracking whether the website is running, and we can verify if users are able to log in and see things running.

From the infrastructure point of view, the availability of the infrastructure at AWS level with Kafka, EC2 instances, and Lambda functions is covered for the monitoring system and infrastructure team. We are catering to both audiences.

Dynatrace's AI-driven Davis engine absolutely helps identify performance issues by showing root cause analysis for us up to 200%; whatever is integrated, if it is visible, it can stitch and show.

The comprehensive application topology visualization in Dynatrace, called Smartscape, has benefited my understanding of system dependencies. Even though Splunk has something similar, since we are using Dynatrace primarily for observability, we can get top-to-bottom visibility, from infrastructure to network to application front end. When integrated based on application requirements, we get a good grasp of what's happening during issues.

Dynatrace could be improved primarily with regards to pricing. These tools are expensive. They have been the pioneers from inception, and they remain at the top of the Gartner chart. The quicker we learn, the better we can serve is what the team believes.

Learning is another aspect that needs improvement, specifically how the tools can educate users. Education needs to be tailored differently for front-end user monitoring, infrastructure-based monitoring, and centralized monitoring teams. The segregation of educating how to use the tool is something we have recommended to all tools teams and product owners.

They could help with more learning capabilities. A monitoring tool will be used by different types of users. One is from the infrastructure point of view. Another is an application developer who wants to see if strings are getting attached and code-based applications are being stitched together. A front-end monitoring person or business wants to know about website and infrastructure availability, and what kinds of dashboards they can create for their comfort. That's how a tool gains visibility and inclusiveness. It depends on the owner of the tool to address these different aspects and let users choose their preferred way to use the tool.

I have nearly six years of experience with Dynatrace.

Regarding the initial setup, while I can't speak for how my company implemented it overall, I can say that the Dynatrace setup is good enough, not an issue. Integration with the cloud is straightforward. In cases of new aspects of cloud, some drilling is needed to determine installation possibilities. That's a challenge in the cloud - whether to integrate with the cloud directly or opt for agent installation. These issues arise when new features or services are enabled in AWS; parallelly, Dynatrace and Splunk and these tools need to adapt to see if these services can be monitored. Users start asking if we can enable new AWS features in our tools. This synchronization should happen at the back end; users should not be involved in that process.

I think Dynatrace's customer service and technical support for this product is good. Out of five, it rates nearly four. This is good enough to expect.

Positive

The monitoring team is able to work without any user intervention, which is appreciable. Comparing to other tools such as Splunk, CloudWatch, or other tools which I have researched for RFPs, I feel DataDog is good enough. However, based on the experience from my users, Dynatrace is more flexible for them.

We deal with APM solutions and monitoring or logging solutions by having Splunk already in place in my environment, along with Dynatrace. We have other tools such as CloudWatch.

I have dealt with Splunk on-call and have all kinds of experience. I have used the PeerSpot platform extensively, and it does help significantly. I will be happy to provide individual product reviews.

On a scale of 1-10, I rate Dynatrace an 8 out of 10.

Amazon Web Services (AWS)

Amazon Web Services (AWS)