Datadog Operator

My main use case for Datadog  is to monitor the logs and capture metrics like CPU metrics, memory, and traces across different services in a cloud-based monitoring system where I initially worked, specifically to debug failing systems and systems which are slow, mainly for monitoring my servers in AWS .

The best features of Datadog  for me are the user-friendly real-time dashboard and its ability to easily integrate with AWS , Azure , Kubernetes , Kafka, and provide a centralized log management system, which gives me excellent visibility into the microservice architecture.

Datadog has impacted my organization by providing a centralized monitoring system so that each person can trace what is happening in the VM servers, and it has given us a centralized dashboard view.

Since adopting Datadog, it has reduced the manual effort by around seven to eight hours per week, making the process completely automated.

Datadog has improved the collaboration across the teams and cross-functional teams, making it very fast and allowing us to easily track what is wrong.

If I could change one thing about Datadog, it would be the pricing, as it has extraordinary functionality, but the pricing is somewhat expensive, and as we increase the number of servers and monitoring services, the cost increases. A more predictable and flexible pricing structure would be beneficial, along with additional customization options and reporting features.

I have been familiar with Datadog for more than two years.

I have not yet faced any frustration with Datadog.

Before I landed on Datadog, I used to review the CloudWatch logs in AWS, and we initially had the tool Checkmk  for monitoring.

When I first implemented Datadog, it took me around thirty to forty minutes for the basic setup because we had a very large application to monitor metrics. After the configuration, the data actually appeared within three to four minutes.

We did not have any formal training on Datadog. Instead, we referred to Google documentation regarding what Datadog is, how to set it up, and what the use cases are, and based on that, we initially set up Datadog.

When evaluating options before choosing Datadog, I compared it with tools such as New Relic  and Grafana  Labs with Prometheus. The main reason I chose Datadog is that it is a single platform where I can see metrics, logs, traces, and alerts, and it easily integrates with Kubernetes  and other services such as Kafka.

Our workflow is both team-wide and individual, as we check the end-to-end observability and the monitoring of our end-to-end application, infrastructure, and cloud services individually as well as in a team.

When I open Datadog, the first thing I do is see the home dashboard, which will have the active alerts and the system health status, as well as listing out all the monitored resources, including the servers, virtual machines, Kubernetes pods, and nodes. I will also see the CPU usage and memory usage, including the disk utilization.

Datadog is used by the cloud infrastructure monitoring team and the application team within the company, and everyone uses it on the same level as I do.

I have not experienced any features during implementation of Datadog that I am not really using in practice.

As of now, for my use case, I am satisfied with what Datadog offers, and I do not wish for any specific features that it currently lacks.

My advice to someone considering Datadog who has a similar workflow to mine is to read the entire documentation and work on it. I would rate my overall experience with Datadog as an eight out of ten.