Hello people! Monitoring is one of the major phases in DevOps lifecycle, It makes sure that application running in your production environments runs continuously without any failure and if any failure comes into the picture then it can be tackled as soon as possible. A simple is to integrate a solution with environments that intelligently monitor, analyze and manage cloud, on-premise, and hybrid applications and IT infrastructure. In today’s blog, we will be discussing a monitoring tool called DataDog.
Datadog is one big dog: its customer list includes the likes of Spotify, PBS, Slashdot, Samsung, Imgur, Coursera, The New York Times, and Ziff Davis.
What is DataDog
DataDog is monitoring and analytics tool that can be used to monitor the events and performance of the application, infrastructure and cloud services. It also can monitor your real-time databases and applications.
Datadog monitoring software is available for deployment on-premise or as a software as a service (SaaS). It supports Windows, Linux, and Mac operating systems. Support for cloud service providers includes AWS, Microsoft Azure, Red Hat openShift and Google Cloud Platform.
It is written in GoLang with the backend support of Apache Cassandra, PostgreSQL, and Kafka. You can integrate it with lots of tools and programming languages. Although It is a paid tool but they do provide you 14 days trial to see how effectively and easily it works. You can see inside any stack with DataDog.
Lets discuss some features provide by Datadog:
- Provides an IT/DevOps team with a single view of their infrastructure (including servers, apps, metrics, and other services).
- Customizable dashboards.
- Alerts based on critical issues.
- Support for over 250 product integrations.
- Can automatically collect and analyze logs, latency and error rates.
- Allows for access to the API.
- Custom live log collections and analytics.
- Supports applications written in languages such as Java, Python, PHP, .NET, Go, Node and Ruby.
Key components of DataDog
- The collector (agent.py), responsible for gathering system and application metrics from the machine.
- The forwarder (ddagent.py), responsible for buffering and communicating with Datadog HQ over SSL.
- Dogstatsd (dogstatsd.py), responsible for aggregating local metrics sent from your code
- Supervisord, responsible for keeping all previous processes up and running.
To conclude this if you are working on a project which consists of lots of technologies and you want to integrate a monitoring tool to make sure it works everything in your system working fine then datadog is a tool which worths a try.