Datadog Reviews

Our use case is mainly deploying into our applications for monitoring/logging observability. We currently have our microservices feed into an actuator that exists in each instance of our application that extends to a local and central Grafana for client and internal visibility. The application we use is Grafana.

Logging captures application and system logs that are ported to each application instance for querying.

Whenever anything occurs that is considered unhealthy from a range of health checks, we have notification rules configured internally and externally for a prompt response time.

We have been able to be a more confident, knowledgeable, and capable team when everything is being ported into a centralized format. Beforehand, knowledge was isolated to individuals. Knowledge in terms of what information represented and where it was led to a lack of confidence. By having everything in one place, rules out that confusion and allows us to respond better to issues.

It also allows for personal growth as our team is learning the application from the ground up, and each person is enhancing their own skills.

The valuable features include the following: 

• We are currently utilizing a decentralized distributed framework for our deployment, including our monitoring/logging observability capabilities. Centralizing them, if contingent on our company privacy guidelines, will be a big help in tracking and responding to issues that come up and have the means to understand the origin of the log management tools that were demonstrated.
• The ability to fiddle around and manipulate how logs are outputted.
• The ability to track AWS Lambda functions, Cloudformation, and Cloudwatch allow someone that is not savvy to dip their toe into understanding their own product.
• The integration into AWS is key as well as our software is currently bound to AWS.

The ability to find what you are looking for when starting out could be improved. It was a bit overwhelming trying to figure out what is the best solution. It led to many prototypes or time spent just perusing documentation. If we were able to select bundles or template use cases, we would hit the ground running quicker.

I've used the solution for one year.

We are using the solution for scaling up the website for market data applications. EC2 and Datadog have enabled high-level monitoring of underlying infra and services.

The Datadog profiler comes in handy to pinpoint issues with resource utilization during peak hours, and traces/log management helps narrow down the root cause.

The network map is crucial in identifying bottlenecks and determining what needs more attention.

Host map helps identify problematic hardware and devise ways to counter issues that arise during scaling, and deploying solutions on the cloud.

While my team is relatively new to Datadog, I already see immense value in switching over to Datadog as the primary APM and NPM tool.

The arsenal of features it offers is bound to come in a clutch when facing production issues, and when finding out what went wrong is crucial.

The network map has helped to figure out the golden signals and optimize the infrastructure.

The synthetics have helped ensure the high availability of arch functions as intended.

The network map is useful. With it, we have the ability to see the data flow across the entire network path across all the applications is highly valuable as the data from this service helps identify network bottlenecks, non-performant applications, and bad endpoints.

This is especially crucial for a high-availability website aimed at market data applications where low latency is crucial.

The host map gives a clear picture of the entire infrastructure, and the ability to switch between logs, metrics, and traces is very handy when it comes to debugging issues on the fly.

I love the ability to install the integrations and agents quickly. This is a well-made product.

To be very fair, I haven't had enough experience with Datadog to pick out improvements.

My involvement with Datadog has largely been positive. I love the simplicity and intuitiveness it offers - even for nontechnical folks who just might be starting out with developing technical chops in their domain.

I've used the solution for three years.

We use the application for our application monitoring, data security monitoring, and log management. What we like about the application is that it helps us to track issues more proactively instead of reactively.

There are other improvements we would like to see.

1. Being able to restrict users from seeing or viewing specific dashboards once they log in

2. They can cut down the prices for Cloud SIEM. It seems very useful, however, the prices are high. Some organizations are finding it difficult to make decisions in terms of getting the tool.

We use the application for our application monitoring, data security monitoring, and log management. It helps us to track issues proactively instead of reactively.

It helps us better manage our logs.

We can effectively track down issues.

We have dashboards that give us an overview of our environment.

The tools I have found useful include the Datadog cloud security posture manager and cloud workload security.

Datadog is a great tool, and we value the services they offer. They should continue expanding and integrating with more third-party apps.

I've used the solution for three years. 

I love its stability.

It is very scalable.

Technical support has been great.

Positive

We previously used AWS.

The initial setup is not too complex.

We've seen an ROI of 50%.

It's a little pricy yet worth it.

We did not previously evaluate another solution.

Ingesting data from various sources to monitor the log metrics of the system and enabling an alert mechanism to notify the teammates if something goes wrong.

More specifically, having Datadog agents as integration to different services provides easy access and management.

The solution has helped out organization by allowing us to ingest data from various sources to monitor log metrics and enabling alert mechanisms to notify teams if something goes wrong.

Datadog agents act as an integration to different services, providing easy access and management.

The solution is useful for ingesting data from various sources. This helps to monitor the log metrics of the system. It has alert mechanisms that can be enabled to notify the teams if something goes wrong.

Datadog offers good integration to different services. It provides for easy access and management.

Ingesting data from various sources to monitor the log metrics of the system can always improve so that, if something goes wrong, the right teams are alerted. 

We've used the solution for close to 1.5 years.

We use a SaaS deployment.

We have deep integration with Datadog for observability and monitoring.

We use everything from APM, logs, and RUM to monitor and dashboards for tracking system health. 

We are trying to move from many different solutions for error tracking/observability to a single platform (Datadog).

We are currently in the process of setting up logging in Datadog in order to maintain our logs better. We are looking to create more insights into the real user flows by using real user monitoring (RUM) too.

We use Datadog quite extensively. I primarily work with APM traces and logs to debug issues and unblock myself in my day-to-day role. I have found the traces and spans most useful in providing details about why certain services are performing poorly.

Datadog provides a lot of value in terms of adding monitoring and observability to our app. There are so many different solutions, it is sometimes difficult to gauge where to start, and I sometimes miss a lot of functionality (such as the very useful error-tracking dashboard mentioned in my review above).

As I mentioned above, we use Datadog quite extensively. In my day-to-day role, I primarily work with APM traces and logs to debug issues and unblock myself. 

I have found the traces and spans most useful in providing details about why certain services are performing poorly.

Additionally, the flexibility to create notebooks and dashboards and fully customize them gives us a lot of power to track the exact services and endpoints we are working on.

Furthermore, we are also using monitoring to page us if things break, and the Slack integration provides us instantaneous feedback on how things are performing.

We need more visibility into the error tracking dashboard. I only learned about it during a demo at Dash Con. That said, it seems to be a very useful tool.

Additionally, we want to export our dashboards and monitors to source control, and there doesn't seem to be any easy way to do so.

I've used the solution for four years.

For logging, we are moving from LogDNA to Datadog to have access to everything in one place. Also, searching and traversing through logs seems easier in Datadog

I did not evaluate others, however, my team probably did.

Datadog provides a lot of value in terms of adding monitoring and observability to our app. There are so many different solutions; it is sometimes difficult to gauge where to start, and I sometimes miss a lot of functionality. For example, the very useful error-tracking dashboard that I just discovered.

We primarily use the solution for the RUM, security monitoring, and streams.

We need to monitor users and what they access. We also need to identify security loopholes and attack patterns and identify and quickly respond to issues.

We can identify pushbacks, and get insight into application components that stack up with each other. We can understand which components, libraries, and code to alert teams.

Using Datadog, we can raise incidents, track incidents to completion, and be able to gather data for reporting and post-mortem.

The solution allows us to track fixes and tracks their test coverage. With it, we get confidence in the fix/improvement phase and be able to provide a response.

The solution has improved the organization by providing good insights into app performance and offering good dashboards.

With it, our company can track fixes and track test coverage. We get confidence in the fix/improvement and are able to provide a response.

I've been able to present data to the team/ management based on the team's dashboards.

It's helped us when we've needed to monitor users and what they access or needed to identify security loopholes and attack patterns. It can help identify and quickly respond to issues.

Datadog allows us to identify pushbacks, and get insight into application components (how they stack up with each other). When we need to know which component, libraries, code, and teams to alert, we can raise and track incidents to completion and gather data for reporting and post-mortems.

The APM and container monitoring are excellent aspects of the solution.

It helps with monitoring users and what they access.

We can identify security loopholes and attack patterns while quickly responding to issues.

Our team can now identify pushbacks and get insights into application components. We can gather data for reporting and post-mortem, and we can track fixes and test coverage. Datadog allows us to gain confidence in the fix/improvement.

I've been able to present data to the team/ management based on the team's dashboards.

We'd like Datadog to make the log storage cheaper. Right now, it makes our life difficult since logs are stored separately.

We should not have to rely on team members to educate themselves in Datadog features. There should be templates that anyone can select, and they should be able to create dashboards easily. This is really slowing us down. It takes time to to explore the full potential of Datadog.

I've used the solution for two years.

After a security incident, we needed to find and migrate to a different cloud provider, and after evaluating different competitors and the skill set of the team, we decided to move to AWS. AWS also enables the team to have finer control over how our apps are deployed and how security and access are managed. By leveraging AWS's functionality, we have increased our application's security and sped up the deployment process. We've even been able to handle higher workloads due to AWS's auto-scaling functionality.

Elastic container services have improved our organization by allowing us to deploy our application. While this problem was not solved by using the elastic container service since we had a previous solution on a different provider, the ease and flexibility of deploys have greatly benefited the ops team and the overall engineering organization. It is easy to use. However, there are many out-of-the-box features like metrics and seeing task definitions that make life easier.

The most valuable feature I have found is the elastic container service. That is where our apps get deployed. This feature is really important to us since, without it, we would not be able to deploy our apps the way we wanted to and would have to use a different vendor. 

This service is also valuable to our team as we have a lot of familiarity with it and adapting to the services from our previous one was fairly seamless. 

Another feature that is very valuable for our team is the IAM. It allows us to control security.

ECS could be improved by including more tutorials for beginners to reduce the barriers to entry. It would help to ensure understanding and learning in terms of how to use a cloud provider. Besides this, there are not any other areas of improvement needed since the service is very straightforward and there is ample documentation.

I've used the solution for one year.

We use metrics to track the metrics of our application. We use logging to log any errors or erroneous application behavior as well as successful behavior. We use events to log successful steps in our pipeline or failed steps in our deployment. We use a combination of all these features to diagnose bugs. 

It makes it much more efficient to look at all the data in one place. This speeds up our development speed so that we can be agile.

This spectrum of solutions has allowed us to track down bugs faster and more rapidly, which allows us to limit revenue lost during downtime. 

It also allows us to accurately record and project current and future revenue by measuring the application's metrics. This way, my team can accurately and rapidly create reports for upper management that are easy to read and understand. 

Datadog is also easy to read by non-technical personnel. This way, if there are any erroneous readings, everybody has a chance to find them.

We use metrics to track the metrics of our application. We use logging to log any errors or erroneous application behavior as well as successful behavior. We use events to log successful steps in our pipeline or failed steps in our deployment. 

We use a combination of all these features to diagnose bugs. It makes it much more efficient to look at all the data in one place. This speeds up our development speed so that we can be agile.

These features are the features that I use the most since it is incredibly difficult to track down intermittent bugs if I were to look directly under the hood in a CLI.

Datadog could make their use cases more visible either through their docs or tutorial videos. There are different implementations of certain features that we utilize to customize Datadog functionality and in that way, we sometimes get results that are not conducive to what Datadog thinks their features' use cases are.

I've used the solution for at least one year.

We have only used Datadog. We did not previously use a different product.