DataDog Training Day -1 Assigment Q/A Set 1

1. What is Observability and its advantage? Observability is the evolution of monitoring where we understand the actual system's state/behaviour. It helps us to build the system better since we get to know the internal condition of the complex system. It collects system data to identify a system's state and performance and is built on top of monitoring. It requires the knowledge of 3 support points - metrics,logs and traces.

It has different advantages and benefits:

1. It aids in recognizing the system's complexity.
2. It assists with recognizing the difficult issues
3. It aids in the troubleshooting of issues at their source.
4. It increased the automation process
5. It aids in processes of self-healing.

6. It makes the user's experience better.

   1. Difference between Observability Monitoring? Observability:
      1. A system has to be designed to be observed
      2. It assists in understand how system is performing
      3. It is passive where we can push/publish
      4. It incorporates metrics,logs and traces
      5. It helps isolate issue at scale
      6. It reduces duration of outages
      7. Generate metrics
      8. It helps to detect why my system has failed
      9. It helps to create resilience Monitoring:
      10. Almost anything can be monitored
      11. It helps to understand if the system is working
      12. It is active where we can pull/collect
      13. It incorporates metrics,events and logs
      14. It helps in challenging at scale
      15. It provides rapid response to outages
      16. Collect metrics
      17. It helps to understand the state of my system
      18. It helps to fix based on past scenarios
   2. What is Datadog? Datadog is a monitoring and analytics tool for large scale applications. It is useful for determining performance metrics and event monitoring for cloud and infrastructure services. It offers metrics, visualization, and alerting for applications' maintenance and optimization. The primary four signals for datadog are latency, traffic, errors, and saturation. It upholds Windows,Linux and Macintosh operating system and it is accessible and it upholds cloud suppliers like AWS,GCP,Azure and so forth.
   3. What is Datadog Agent and How it works?
   4. The Datadog Agent is software that runs on hosts.
   5. It sends events and metrics from hosts to Datadog, where monitoring and performance data can be analyzed.
   6. The Datadog Agent's source code can be found on GitHub at DataDog/datadog-agent because it is open source. Working Mechanism: The Agent should be installed completely. On the other hand, Amazon Linux, CentOS, Debian, Fedora, Red Hat, SUSE, and Ubuntu all come with a separate DogStatsD package. The standalone DogStatsD package is installed with the Agent one-line install command except every occurrence of datadog-agent should be replaced with datadog-dogstatsd. A Docker image is available in the DogStatsD6 Docker image repo. In containerized environments where DogStatsD runs as a sidecar or in environments with a DogStatsD server but no full Agent functionality, this package is used.
   7. Component of Datadog Agent and short intro for each Datadog Agent is composed of a main process responsible for collecting infrastructure metrics, logs, and receiving DogStatsD metrics. The main components to this process are: The Collector is in charge of running checks and collecting metrics. The Forwarder sends payloads to Datadog. Two optional processes are spawned by the Agent if enabled in the datadog.yaml configuration file: Datadog Agent is composed of a main process responsible for collecting infrastructure metrics, logs, and receiving DogStatsD metrics. The primary elements of this procedure are: The Collector is in charge of running checks and collecting metrics. The Forwarder sends payloads to Datadog. If the datadog.yaml configuration file is set to "enabled," the Agent will launch two optional processes:

A process for collecting traces is the APM Agent (enabled by default).
The Process Agent is a method for obtaining live process data. In the absence of available containers, it is disabled by default.

Collector
The collector collects all standard metrics every 15 seconds. Agent v6 embeds a Python 2.7 interpreter to run integrations and custom checks.

Forwarder
The Agent forwarder send metrics over HTTPS to Datadog. Buffering prevents network splits from affecting metric reporting. Metrics are buffered in memory until a limit in size or number of outstanding send requests are reached. Afterwards, the oldest metrics are discarded to keep the forwarder’s memory footprint manageable. Logs are sent over an SSL-encrypted TCP connection to Datadog.

DogStatsD
In v6, DogStatsD is a Golang execution of Etsy's StatsD metric total daemon. It is utilized to get and turn up inconsistent measurements over UDP or Unix attachment, accordingly permitting custom code to be instrumented without adding inertness.

GUI
In the datadog.yaml file, you can set the port on which the GUI runs. Set the value of the port to -1 to disable the GUI. For Windows and macOS, the GUI is empowered of course and runs on port 5002. For Linux, the GUI is handicapped as a matter of course.
When the Agent is running, use the datadog-agent launch-gui command to open the GUI in your default web browser.