AWS Auto Scaling monitors applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost. The vendor states that using AWS Auto Scaling, it’s easy to setup application scaling for multiple resources across multiple services in minutes.
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Dynatrace
Score 8.4 out of 10
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Dynatrace is an APM scaled for enterprises with cloud, on-premise, and hybrid application and SaaS monitoring. Dynatrace uses AI-supported algorithms to provide continual APM self-learning and predictive alerts for proactive issue resolution.
It is well suited for scaling up our cloud virtual machines to handle the increase or decrease in workload. It really helps us to satisfy the demand because it doesn't take lot of time to spin up new machines. If there is unpredictability in the demand it is very useful. But it is a over kill if demand is consistent.
Dynatrace is well suited to a number of tasks. It is important to determine who the end users are and gather good information to tailor their experience accordingly. For instance, business/marketing should not have access to some of the more technical data, and business metrics can be a distraction for IT operations personnel.
We loved Dynatrace's ability to show the data flow - from the front end points through the back end points straight to the database and various API's. It was advanced in its data visualization. This is useful for debugging - showing when/where the errors are. It can even enable non-technical individuals in the corporation to help debug
Dynatrace has some great highly customizable integration options as well as monitoring. You can configure your layout & integration options to create custom monitoring alerts for your applications performance. Further you can increase the extensibility of using a REST API on your architecture.
Some advanced dev-ops systems are utilizing Kubernetes/docker aswell as Node.JS - Dynatrace was able to log and help understand all of our dev-ops needs. It gave us native alerts based off of deviations from the baseline that we set during initial configuration. These metrics are priceless.
Dynatrace does not monitor easily on a C-based application.
The way DPGR is addressed by Dynatrace is not very complete, and not clear. One thing is to mask the IP and request attributes but is not enough, the replay session feature is great but raises serious questions about user tracking.
We have already renewed our purchase with the company. They make it easy for us to get a temporary license for our contingency site that is only used for testing twice a year. We are expanding our license with for this tool. We find it very useful and will renew it again.
We use AWS auto scaling for scaling up our cloud virtual machines to handle the increase or decrease in workload. It really helps us to satisfy the demand because it doesn't take lot of time to spin up new machines. I gave the rating 10 because it really does help you to handle the sudden spike in number of requests.
Dynatrace is great to use once you understand how to use it correctly and get used to the layout of it. While I do not actively use it every day, whenever I do use it, I do have to get refamiliarized with it. However, once you have your dashboards setup correctly with the data that you want to see when you first login to Dynatrace, it's amazing.
Given that Dynatrace has become an informal industry standard, the plethora of information available on forums is massive. Most problems or roadblocks you come across are most likely (almost certainly, in fact) already solved and solutions available on these forums. The tech support at Dynatrace is also quite good, with prompt and knowledgeable people at their end.
Synthetic Monitoring automatically does what other products do only through the use of other tools or through the development of user applications that still have a high cost of maintenance. The other products are not immediately usable and require many customizations. Through the use of configuration automatisms, you can be immediately operational and, in our case, we detected several imperfections in the applications.