What is Datadog?
Who uses Datadog?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Datadog in their tech stack.
We use Datadog to centralise our log outputs, monitor our hosts and set alerts for our tools.
Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.
Current Environment: .NET Core Web app hosted on Microsoft IIS
Future Environment: Web app will be hosted on Microsoft Azure
Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server
Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.
Please advise on the above. Thanks!
My team is divided on using Centreon or Zabbix for enterprise monitoring and alert automation. Can someone let us know which one is better? There is one more tool called Datadog that we are using for cloud assets. Of course, Datadog presents us with huge bills. So we want to have a comparative study. Suggestions and advice are welcome. Thanks!
We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.
We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.
We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.
You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?
We use AppOptics. I am curious what are the current leaders for APM for small companies (50 employees) that use Python, MariaDB, RabbitMQ, and Google Cloud Storage. We run both Celery and Gunicorn services. We are considering Datadog or some other deep code profiling tool that can spot I/O, DB, or other response time/request rate issues
I see StatsD is commonly used in conjunction with Datadog. In fact, Datadog even has their own StatsD daemon (called DogStatsD) embedded in the DataDog agent. Can someone explain to me what it is that StatsD gives you which you don't already have with Datadog's APM and distributed tracing functionality?
- 14-day Free Trial for an unlimited number of hosts
- 200+ turn-key integrations for data aggregation
- Clean graphs of StatsD and other integrations
- Slice and dice graphs and alerts by tags, roles, and more
- Easy-to-use search for hosts, metrics, and tags
- Alert notifications via e-mail and PagerDuty
- Receive alerts on any metric, for a single host or an entire cluster
- Full API access in more than 15 languages
- Overlay metrics and events across disparate sources
- Out-of-the-box and customizable monitoring dashboards
- Easy way to compute rates, ratios, averages, or integrals
- Sampling intervals of 10 seconds
- Mute all alerts with 1 click during upgrades and maintenance
- Tools for team collaboration