Grafana Reviews

We manage log processing with Grafana because we found that it is much easier for us to manage it on our infrastructure on AWS. We can maintain all the things we do not need. DataDog told us we have to wait and that they need to provide features we need to develop, so it does not suit our needs.

We are still using DataDog, but for important assets that we need to analyze the logs, we send it to Grafana.

The challenges we face with DataDog compared to Grafana include the need to analyze very important brands, network trafficking, and maintaining many websites, most of which are very important domains that cost a lot of money, so we are getting attacked each day and we need to analyze all of the logs. Sometimes we have false positives and things similar to that, so we have to make sure that we are doing the correct decision of blocking or trying to mitigate attacks. Using the logs with Grafana it is much easier for us to analyze rather than DataDog. DataDog has their own language and they want you to plot things with their own vocabulary. We do not have time to memorize things. We especially wanted to use something that was open source at the beginning, and then other people started using it, took that product and modified it for extra cost, but it is a better solution for us.

We switched from DataDog to Grafana because we wanted to reduce the logs costs, as we are streaming approximately five million logs or even less.

We can find information with Grafana much more quickly compared to DataDog because it was open source and there was extensive documentation about it, enabling us to fetch data or information much more quickly using AI tools. With DataDog, they always wanted us to have a meeting or talk with us on a call. It was redundant. We just wanted to get to the solution without making a big deal out of it.

Grafana saves us hours compared to DataDog. It takes about two weeks to figure out what is going on with DataDog, but with Grafana, we just started to initialize the service, had a few issues, fixed them, and that was it. I did not have any major problems that forced me to halt everything in my work. It cost me hundreds of hours with DataDog because I needed to see all the documentation and all the special caveats they have there.

We actually work with BMC and similar solutions, and while I wouldn't say that they are the same as a full, fully-fledged APM, we're working our way up using open source and everything, so I don't know how far we'll get with that. We're a government agency, and although these costly tools are very efficient and useful, it's hard for me to convince the organization about that, especially since we're not allowed to use public cloud solutions, so we have an on-prem solution, which makes us a demanding customer.We use BMC Helix with the AIOps solution, and while all of these are public domain knowledge because we have to be open with how we procure and purchase our services, I am not the one who would purchase such a system. I would be the customer to it and someone else would host it, while I work more with observability, such as the Grafana stack and the cloud-native solutions.We're working with Grafana more actively now for maybe a couple of years, and we are looking at Tempo and the tracing solutions to see if it will scale. We have metrics back-ends and everything, and we're basically late-stage discovery, early-stage implementation at large scale, with 1,400 people in our tech organization and a 14,000 people organization, where the technology side is just 10%.We get alerts from the alerting tools, and we act on them. I'm looking now at logs, Grafana, trace profiles, and while we're not yet into AI/ML and root-cause analysis, we do focus on alerting and, to some extent, plugins and app observability, mainly utilizing the basics for now.

We use Tempo for tracing and, of course, alerting and monitoring metrics. We've tested some features but we're only using a few of them actually, so I can't provide the full range of what different tools Grafana offers as an open-source project, but it's definitely useful for monitoring, alerting, logs, and analysis.Grafana integrates with some of the tools we've chosen beforehand, and while I'm not the right person to elaborate on this since I'm more focused on managing big teams, the teams working with it seem happy. We used to have performance issues, which could be due to running it in an OpenShift environment, but functionality-wise, it seems to be useful, albeit not offering the same power tools as APM such as AppDynamics.

For my personal usage, I do use Grafana, and it does make life easier for solving stuff.

The main benefits I have seen from using Grafana in my day-to-day activities is the visualization of the metrics, specifically Dora Metrics. Instead of saying 'I feel the team is developing bad or slow,' you can actually have metrics to support that. You can actually show that this team could work better here, in this case, or needs to focus more on peer reviews or on deployments. The visualization of the data is key.

My main use case is monitoring. We use a lot of different products for monitoring, but Grafana is specifically for monitoring Kubernetes. We use Grafana mainly for Prometheus.

The features I appreciate most are the dashboards and the integrations with multiple data sources. The feature that sets Grafana apart from its competitors is how easy it is to set up data sources. The integration helps our organization in centralizing and analyzing data from diverse sources.

The primary use case for Grafana is to display data from Prometheus in a graphical user interface for easy monitoring and analysis of metrics. It is used for end-to-end tracing of applications and integrates with AWS services.

Grafana improves the organization by showing data from Prometheus in a user interface.

I use Grafana mostly for monitoring purposes, such as tracking errors or alerts. When certain thresholds of warnings or errors are breached, Grafana sends me an alert because I am supplying it with data. Another use case I have is monitoring CPU usage and bandwidth.

I find Grafana beneficial due to its numerous plugins. Its alerting feature is effective because it allows me to set thresholds to send an email if a certain threshold is met. User management is good, and the administration features enable me to control user access.

I use Grafana to deploy OpenVideo and to write an article on Medium on how we can deploy some open source projects with CloudFormation.

I have installed Grafana to see custom metrics like the number of connected users, number of rooms, and CPU usage.

My main use case is the dashboard. For example, we have screens created in the last step, and we integrate Grafana with that so that we can see the dashboards with the data. What does the traffic look like? What do the errors look like? So the is our main use case. 

The most valuable aspect is customization. There are many customizations possible, so I like that.