AppDynamics Serverless APM for AWS Lambda

Splunk AppDynamics  allows us to understand the mean time to resolution and decrease it by easily understanding the dependency of the full application flow map. For root cause analysis and other hidden aspects, we can see how code quality performs. SQL queries can be easily evaluated for quality, and when code quality is not good, we can identify slowness in specific classes and methods. We can see which parts of methods and SQL queries are facing slowness issues. After that, we can develop and change code, modify database queries, and easily see in the product environment without needing to debug facilities. We can see in real time whether code changes are affecting the system positively or negatively. There are many different advantages, and we can separate proactive and reactive sides.

When we collect different method parameters with the essentials of monitoring APM  tools, we can easily combine business and operational development cycles in a single pane. For example, when development teams make process improvements to code to add new features to transactions, we can easily see how this feature affects customer experiences as performance metrics. If we can collect this kind of data, we can also easily combine business and operation metrics. For example, with a loan application from the customer side, such as a bank customer making a loan application over their mobile or internet banking application site, we can easily see how many successful transactions occur in real time from a business perspective, not just from the performance side. If we can collect these metrics, we can combine all performance and business metrics in a single pane, giving customer sites a very different and big picture view.

Splunk AppDynamics  works for Java, .NET, .NET Core, Node.js, and PHP applications. We also work with some customers using SecureApp features, and customer feedback has been really valuable for us. From my customers' experience with this feature, the feedback is really positive. In the software development lifecycle, penetration testing or security testing before getting a project into live production environment is a very important process. You have to conduct penetration testing before going live with your project. However, this kind of penetration testing is a bit reactive and offline because you only perform this penetration testing from a synthetic point of view, for example weekly, monthly, or quarterly. With Splunk AppDynamics SecureApp solution, we can easily see our application's vulnerabilities, attacks, and exploits in real time. We can also see any vulnerability, even zero-day attacks, easily after they occur. This is a really cool and differentiating feature, though it is a very new feature in the APM  market, almost two years old. Because of that, it is not well-known, but when we demonstrate it to customers in POC or demo sessions, most customers are impressed and want to try it in POC. After POC, some customers want to buy this feature while getting the APM solutions as well.

We can monitor what kind of vulnerabilities exist in the code and can easily show the business risk in the environment by making a business score, not relying on MITRE or CVE codes. The scoring also works from a business perspective. For example, if we have one vulnerability that may be medium severity, our internal scoring mechanism increases the business risk if the application touches databases or other inside applications. If the application is not communicating with other applications, databases, or other sources, the business risk may be lower than the other example because of the application's touching points. This is a really cool feature. We are not only reporting these vulnerabilities, but we are also blocking these attacks in real time. For example, when a Log4j2 vulnerability occurs on the system or any zero-day attacks happen, Splunk AppDynamics easily tags and understands this kind of attacks. If desired, it can easily block the application's attacks from the APM perspective. This is a really game-changer in my opinion.

I think one of the really strongest features of Splunk AppDynamics is the end-user experience monitoring. We have a really differentiated capability over our competitors. We can easily adapt our solution to the customer's application, internet banking solutions, or IoT devices all over the world. For example, when you get a new Volvo from any Volvo shop, that car has a built-in Splunk AppDynamics light agent to track their connected car applications. To give a specific example, Audi, Volvo, and four years ago BMW also use Splunk AppDynamics light agents to monitor IoT devices and connected car applications. Mercedes may have this kind of agreement as well. In summary, we can easily monitor mobile devices, including Android and iOS, browser-based applications, and also IoT devices. For example, in Turkey, I personally use IoT monitoring with my customers. We work with banks, and most customers monitor their ATM devices and POS devices via Splunk AppDynamics agents. I have personally implemented the IoT agent or Splunk AppDynamics agent into ATM devices and POS devices, as well as for some cinema companies' kiosk systems.

I can mention two different things. First, Splunk AppDynamics is mostly compared with the Dynatrace  solution because they are a really good solution. They offer on-premises options as well. I know Datadog  is another good solution, but it only works with SaaS solutions. New Relic  and Grafana  are also good solutions, but Splunk AppDynamics and Dynatrace  are the only on-premises options in the marketplace. Because of that, I want to compare with Dynatrace. Dynatrace has a OneAgent mechanism, while Splunk AppDynamics has a smart agent mechanism. The idea is quite similar, but when you use the Dynatrace OneAgent solution, because you are giving administrator and root rights, it is a bit easier but unsafe. For Splunk AppDynamics, you do not need to give the agent administrator or root privilege, but because of that, its capabilities are a bit limited. I cannot directly say this is a negative thing because it depends on the perspective. For example, if you really stick to security mechanisms, security teams can say that Dynatrace is easy to install and monitor, but from the security perspective, it is terrible and awful because you are giving full administrator and root privileges to Dynatrace. Splunk AppDynamics could improve their installation process, which would be an incredible thing on Splunk AppDynamics' side.

Second, most products, even Dynatrace, Splunk AppDynamics, and Datadog , are always saying they are making AIOps , root cause analysis, and anomaly detection features, but even Splunk AppDynamics, these kinds of features are not working fine because of the nature of the metrics. Most of the customers are not supplying the hygiene of metrics. If you do not supply or make your environment's metrics hygienic, you cannot give the AIOps  perspective to customers. The statement that these vendors can make root cause analysis automatically or have automatic detection features and capabilities cannot be truthfully said. To sum this up, this is not only a Splunk AppDynamics problem. From my personal perspective, this is all APM vendors' problem. The features that all these APM vendors need to improve are the AIOps features. These are really at the beginning of the AIOps era. Everyone is talking about AI, and it turns out to be a common hype in the technology market. We may see the real effect of this AIOps era maybe two or three years from today.

It is almost at the beginning of the story. When I started with Splunk AppDynamics, there was no acquisition between Splunk and AppDynamics, and AppDynamics was also its own company. This has been 14 years.

Implementation can be divided into two different parts. One part of the implementation process goes over the controller side, which can also be called the control plane side. One engineer is enough to install and prepare within two hours. However, the agent side is a bit more complicated because it depends on the customer's situation. If the customer has more than 100 or 1,000 different servers, mostly in production environments, we need to agree on when we deploy agents because we need to restart their applications. This creates some outages in their production environment. First, we need to agree on the timeline and project plan. It depends on the customer's decision. If we have a chance, at one time, we can also deploy more than 500 different agents at the same time, maybe within half a day, because we have really good playbooks and automation scripts working over Ansible , Chef , Puppet , or different automation tools that we can easily integrate. Implementation is easy, but the agent side depends on the customer's decision based on their project coverage or decision. It can take two days, or maybe two weeks or two months, depending on how big their environment is and how many licenses they get. For example, if they get more than 1,000 licenses for more than 1,000 hosts, it depends on their decision and project plan. It can take two months, one month, three weeks, or two weeks. It is a very variable thing.

I am working for my own business. Previously, I was in my former company, but I quit and built my own company. We are operating in the same area, and nothing has changed significantly in my life.

Over the last 14 years, we have made maybe more than 500 different installations, maybe much more than that. I do not know exactly, but in the last four years, we have prepared our own scripts and playbooks. It is really very simple to build a Splunk AppDynamics platform over the on-premises data center. Even if the customer wants to use the high availability option, if they have a limited environment or limited hardware resources, we can easily build all these components in one server. Because of that, it can be very simple when working only with one server. However, most customers in Turkey, including fintech-based, banking, government, and some really huge enterprises, need to use the high availability options, which means using more than six different separate servers. With our playbook and the solution's flexibility, Splunk AppDynamics is very flexible for this kind of model, and it takes no more than two, three, or four hours.

I would rate this solution an 8 out of 10.

We use Splunk AppDynamics  for infrastructure, application, and Kubernetes  monitoring, as well as private synthetic agents. We use it for all purposes, including as an extension for log monitoring and every extension. We develop any custom extensions as needed.

At present, I am actively working on a private synthetic agent in Splunk AppDynamics , which we have deployed internally in our IKP platform. I am currently working with that by developing Python Selenium scripting.

I have experience with Splunk AppDynamics' Digital Experience Monitoring, including end-user monitoring. For the functionality part in Splunk AppDynamics, I am comfortable with it.

I find all functions valuable in Splunk AppDynamics because I am from the AppDynamics team in my current company. We help with user queries, and the value depends on the use cases, which might be different for each user.

My impression of Splunk AppDynamics' AI-powered anomaly detection is that it functions very well. The anomaly detection gives the exact root cause of what is happening on our server, and for analytics, for every application, we mostly try to configure the analytics to visualize all the things.

Splunk AppDynamics is useful for me and everything is working out fine. Previously, with log monitoring, I might have been a bit unsatisfied with that. As it is integrated with Splunk, that is also very good.

Splunk AppDynamics gives all the metrics that report to AppDynamics, and they work very well and provide precise information from the server. For application performance, it also gives all the business transactions very efficiently. If there are any other things, we can configure them by an extension or manually.

For now, all secure application features in Splunk AppDynamics are good with me. But in the future, I need to go through all of that because while I have experience, it majorly depends on the use cases. I also need to acquire more knowledge on some of the concepts of AppDynamics.

Splunk AppDynamics is very efficient in all those areas. Compared to the cost, it is also very cost-efficient, because that is the main thing for every organization. For all the things concerning metrics, it is also very good for now.

I am somewhat aware of the data collection features in Splunk AppDynamics, but for now, I have not worked on it.

For now, Splunk AppDynamics is a very efficient tool. However, we have a slight complexity where for infrastructure we have to go to one agent, for the database we have to go to one agent, and for the application we go to another agent. For applications also, depending on the type and nature of the application, we have to go to different agents. Different agents mean we need to install different agents. This is something I find a bit more complex.

I have been working with Splunk AppDynamics for two years.

I would rate the stability of Splunk AppDynamics a nine or ten because it gives efficient monitoring.

I would rate the scalability in Splunk AppDynamics an eight.

Before working with Splunk AppDynamics, I was working with Dynatrace  and DataDog.

I have been working with Splunk AppDynamics for the past two years. Before, I was working with Dynatrace  and DataDog. When I came to Splunk AppDynamics, I found it a bit more complex because for everything, we have to go to different agents. I found this part a bit difficult, but since I have been working with it for two years, I am becoming habituated to it.

With DataDog, the UI is very simple. If you compare Splunk AppDynamics and DataDog, the UI accessibility and all the things are very simple there. There will be only one agent present where we deploy all the things. The configuration of alerting policies, which you configure in Splunk AppDynamics, are also very easy there.

I can confirm that I am still working with Splunk AppDynamics.

I am using Splunk because it might be different from Cisco to Splunk. For now, everything is good with me. I think Splunk AppDynamics is more evolved, so for now, all things are good with me. My overall rating for this review is nine out of ten.

Splunk AppDynamics  is currently being used in my organization for APM , application performance monitoring. We have Java-based and .NET-based agents that fetch the APM  metrics onto Splunk AppDynamics . We have a Splunk AppDynamics SaaS offering that is ongoing. A few years ago, approximately two to two and a half years ago, we also used Splunk AppDynamics for platform monitoring and cloud platform monitoring. However, right now, it is mostly APM.

The auto-discovery and anomaly detection features are particularly valuable to us. The baseline variance methodology for anomaly detection in APM monitoring has helped us troubleshoot and triage problems where applications experience high surges of traffic and go down suddenly. This kind of view has been very helpful to us in the past when we ran into issues.

We have used Splunk AppDynamics for business transaction monitoring. The business transactions feature is the feature I applaud the most in Splunk AppDynamics. The business transactions feature has helped us stay current with the trends in traffic. We were able to separate successful transactions from non-successful transactions, such as transactions with 200 error codes and 500 error codes. This capability has been very beneficial to us.

I like the view of business transactions timeline that Splunk AppDynamics provides. This view has helped me troubleshoot many production issues. When you select an application, there is a business transactions view that I find very valuable.

The auto-discovery and anomaly detection features are outstanding. The baseline variance methodology for anomaly detection in APM monitoring has helped us troubleshoot and triage problems where applications have high surges of traffic and go down suddenly.

I have not used Splunk AppDynamics rigorously in the past one year for platform monitoring. For application monitoring, we are quite satisfied. The reason why we have not used it for platform monitoring is because we have a VMware product called Tanzu, and they do not integrate very well with Tanzu. I think the reason behind this is that VMware has their own monitoring solution and they wanted to promote it, which is a business and political consideration rather than a feature issue. Therefore, I have not explored the various dashboard features of Splunk AppDynamics. I would think Splunk AppDynamics could do a better job in creating out-of-box dashboards for Kubernetes-based cloud applications. This is something I would recommend.

The user interface is great and good. If you could provide more out-of-the-box dashboards, as other monitoring systems do, that would be a really good addition to the solution.

For SaaS, we have not experienced stability issues. After we moved to SaaS, we have not had any problems. Earlier, we used to do agent updates, but now that we have moved to SaaS, we no longer need to.

Regarding scalability, I cannot really comment on it. I have not really scaled up recently with our Splunk AppDynamics solution. It has been pretty stagnant. However, based on my interactions, it was pretty decent. I would rate it around six or seven.

We did have to contact technical support regarding a specific issue when we were doing blue-green deployments. When the app changed from blue to green, the app name changes, but the subsequent app name change was not reflected on our Splunk AppDynamics console unless we restarted the app. We worked with the vendor and it turned out the metric being sent out by our nozzle to Splunk AppDynamics was the problem. The vendor was very helpful and we had great vendor interaction whenever we worked with them.

I have used Dynatrace , ELK, Elastic APM, Grafana , and Arya. These are all other observability products I have used. When I compare them, I still prefer Splunk AppDynamics for baseline detection, baseline anomaly detection, and business transactions. However, I prefer Grafana  dashboards that come out of the box for Kubernetes  or virtual machine-based cloud offerings.

The initial deployment of Splunk AppDynamics was easy.

The vendor worked with us hand in hand during the implementation. Overall, we were able to complete it in less than two days.

One person can do the deployment as it is not that complex. Once we have the template and configuration everything set, it flows pretty smoothly.

We do not use OpenTelemetry .

We have not used Splunk AppDynamics for any code reviewing.

I have not used logins and checkouts for business transactions.

I have never tried using it in diagnosing any performance issues.

I am not aware of the pricing as that is above my level of involvement.

My overall review rating for Splunk AppDynamics is eight out of ten.

I use Splunk AppDynamics  as our monitoring tool, and it has been effectively used in the last five years to identify major issues and rectify them without causing a major impact to the website.

Splunk AppDynamics  tells us what the top 10 queries are which are actually contributing to the load on the database, and it gives us the view of what exactly the query does, whether it uses more DB CPU or is contributing to the roll-up contention on the database. These details provide great insights, making it one of the good features from Splunk AppDynamics.

When it comes to the front end with my Node.js and React.js applications, it doesn't capture much of the details. The improvements I made were mainly around the Java agent side in our app layer, but it was lacking detailed information on the front-end layer. This is a disadvantage, and it could be improved from Splunk AppDynamics' perspective.

I have been working with this solution for the last five years.

It has a cost associated with it, but it is scalable only from a scalability perspective. It is scalable.

I have two different types of technologies used in my project. One of the projects was using a different monitoring tool called Dynatrace , a major competitor for Splunk AppDynamics. Discussions were going on whether I wanted to replace Splunk AppDynamics with Dynatrace , and a decision was made to replace Dynatrace with Splunk AppDynamics because of the capability it has with custom metrics and Java agents. However, discussions are now ongoing about potentially replacing Splunk AppDynamics with Dynatrace. I am using these two major tools in my ecosystem: Splunk AppDynamics and Dynatrace.

I monitor all the business transactions such as basket and add to basket via Splunk AppDynamics. I have set up and diagnosed those transactions.

For the others, they get captured as part of the catch-all transaction itself. In case I need them to be investigated, I use filters to identify the transaction specific to the particular BT.

I have not used auto-discovery as a tool such as analytics and the tracing, trace component, and diagnostic traces much. However, I use historical data to understand where exactly the issue is and try to rectify it.

I have not used the anomaly detection and root cause features.

I have not used troubleshooting as a feature.

Regarding Digital Experience Monitoring, I will lose the limit, causing licensing issues. I identified what pages and applications I don't want to monitor and removed them to cope with licensing. Going beyond the license means paying additionally.

I was not using the Secure Application feature.

Currently, I am not using an on-premises environment, so I cannot comment more on the on-premises side of things as I am using a cloud-based application in all areas.

My overall rating for Splunk AppDynamics is 9 out of 10.