Datadog Reviews

We use Datadog for log aggregation and management, metrics aggregation, application performance monitoring, infrastructure monitoring (serverless (Lambda functions), containers (EKS), standalone hosts (EC2)), database monitoring (RDS) and alerting based on metric thresholds and anomalies, log events, APM anomalies, forecasted threshold breaches, host behaviors and synthetics tests.

Datadog serves a whole host of purposes for us, with an all-in-one UI and integrations between them built in and handled without any effort required from us.

We use Datadog for nearly all of our monitoring and information analysis from the infrastructure level up through the application stack.

Datadog provides us value in three major ways:

First, Datadog provides best-in-class functionality in many, if not all, of the products to which we subscribe (infrastructure, APM, log management, serverless, synthetics, real user monitoring, DB monitoring). In my experience with other tools that provide similar functionality, Datadog provides the largest feature set with the most flexibility and the best performance.

Second, Datadog allows us to access all of those services in one place. Having to learn and manage only one tool for all of those purposes is a major benefit.

Third, Datadog provides significant connectivity between those services so that we can view, summarize, organize, translate and correlate our data with maximum effect. Not needing to manually integrate them to draw lines between those pieces of information is a huge time savings for us.

We primarily use the solution for a variety of purposes, including:

• Watching RUM data for frontend site, using LCP and INP metrics to compare across the old and new architecture to inform rollout decisions.
• Watching APM data for backend services, observing how the backend server reacts (CPU util, memory, requests/second) to make sure the backend can handle the load.
• Using Datadog CCM during our free trial period to get visibility over our AWS spend across accounts and resources and looking at recommendations and acting on those.
• Browsing the service catalog to look at the current state of services that are running and what resources it uses. 

This provides a single place to find monitoring data. Prior to DD, we had some metrics living in New Relic, some in Grafana, and some in Circonus, and it was very confusing to navigate across them. Understanding different query languages is challenging. Here, there's a single UI to get used to, and everything is so sharable.

DD has led to teams making more decisions based on data that they observe about their service metrics and RUM metrics. I've seen decisions get made based on what has been observed in DD, and less based on anecdotal data.

We utilize Datadog to monitor both some legacy products and a new PaaS solution that we are building out here at Icario which is Micro-Service arch. 

All of our infrastructure is in AWS with very few legacies being rackspace. For the PaaS we mainly just utilize the K8s Orchestrator which implements the APM libraries into services deployed there as well as giving us infra info regarding the cluster. 

For legacies, we mainly just utilize the Agent or the AWS integration. With APM in specific places. We monitor mainly prod in Legacy and the full scope in the PaaS for now.

Datadog has greatly improved the time needed to investigate issues. Putting everything into a single pane of glass. Allowing us to get ahead of infra/app-based issues before they affect customer experience with our products. 

Outside of that, the ease of management, deployment of agents, integrations etc. has greatly helped the teams. There isn't much leg work needed by the devs to manage or deploy Datadog into their stacks. This is with the use of Terraform, pipelines and the orchestrator. All in all, it has been an improvement.

We use Datadog for monitoring and observing all of our systems, which range in complexity from lightweight, user-facing serverless lambda functions with millions of daily calls to huge, monolithic internal applications that are essential to our core operations. The value we derive from Datadog stems from its ability to handle and parse a massive volume of incoming data from many different sources and tie it together into a single, informative view of reliability and performance across our architecture.

Adopting Datadog has been fantastic for our observability strategy. Where previously we were grepping through gigabytes of plaintext logs, now we're able to quickly sort, filter, and search millions of log entries with ease. When an issue arises, Datadog makes it easy to track down the malfunctioning service, diagnose the problem, and push a fix.

Consequently, our team efficiency has skyrocketed. No longer does it take hours to find the root cause of an issue across multiple services. Shortened debugging time, in turn, leads to more time for impactful, user-facing work.

We have multiple nodes integrated into our Azure infrastructure and our AKS clusters. These nodes are integrated with traces (as APM hosts).

We also have infrastructure Hosts integrated to see the metrics and the resources of each hosts mainly for Azure VMs and AKS nodes. Additionally, we also have hosts from our VMs in Azure which act as Activemq and we integrate them as messaging queues to show up in the Activemq dashboard.

We have recently added Activemq as containers in the AKS and we are also integrating those as messaging queues to show up in the Activemq dashboard integration 

Logs are great. Having all services with different teams sending the logs to Datadog and having all logs in the same place is very helpful for us to understand what is going on in our app; filtering of the logs a huge help and adding special custom filters is easy, filters are fast. Documentation is better than average, with little room for improvement.

Dashboards are simple, and monitors are very easy to configure and get notified if something is wrong.

With the aggregated logs, we can now see logs from other systems and identify problems in other areas in which we had no visibility before.

We use Datadog for monitoring the performance of our infrastructure across multiple types of hosts in multiple environments. We also use APM to monitor our applications in production. 

We have some Kubernetes clusters and multi-cloud hosts with Datadog agents installed. We have recently added RUM to monitoring our application from the user side, including replay sessions, and are hoping to use those to replace existing monitoring for errors and session replay for debugging issues in the application.

We have been using Datadog since I started working at the company ten years ago and it has been used for many reasons over the years. Datadog across our services has helped debug slow performance on specific parts of our application, which, in turn, allows us to provide a snappier and more performant application for our customers. 

The monitoring and alerting system has allowed our team to be aware of the issues that have come up in our production system and react faster with more tools to debug and view to keep the system online for our customers.

We use Datadog across the enterprise for observability of infrastructure, APM, RUM, SLO management, alert management and monitoring, and other features. We're also planning on using the upcoming cloud cost management features and product analytics.

For infrastructure, we integrate with our Kube systems to show all hosts and their data.

For APM, we use it with all of our API and worker services, as well as cronjobs and other Kube deployments.

We use serverless to monitor our Cloud Functions.

We use RUM for all of our user interfaces, including web and mobile.

It's given us the observability we need to see what's happening in our systems, end to end. We get full stack visibility from APM and RUM, through to logging and infrastructure/host visibility. It's also becoming the basis of our incident management process in conjunction with PagerDuty.

APM is probably the most prominent place where it has helped us. APM gives us detailed data on service performance, including latency and request count. This drives all of the work that we do on SLOs and SLAs.

RUM is also prominent and is becoming the basis of our product team's vision of how our software is actually used.

The primary use case for this solution is to enhance our monitoring visibility, determine the root cause of incidents, understand end-user behaviour from their point of view (RUM), and understand application performance.

Our technical environment consists of a local dev env where Datadog is not enabled, we have deployed environments that range from UAT testing with our product org to ephemeral stacks that our developers use to test there code not on there computer.  We also have a mobile app where testing is also performed.

Datadog has greatly improved our organization in many ways. Some of those ways include greater visibility into application performance, understanding of end-user behavior, and a single pane of glass view into our entire infrastructure.  

Regarding visibility, our organization previously used New Relic, and when incidents or regressions happened, New Relic's query language was very hard to use. End-user behavior in RUM has improved our ability to know what to focus on. Lastly, the single pane of glass view with maneuvering between products has helped us truly understand root causes after incidents.