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Karan Singh Karan is a highly experienced DevOps Engineer with over 13 years of experience in the IT industry. Throughout his career, he has developed a deep understanding of the principles of DevOps, including continuous integration and deployment, automated testing, and infrastructure as code.

12 Best Practices for Effective Monitoring and Observability

5 min read

In today’s digital age, monitoring and observability are critical components of any software or application development process. Effective monitoring and observability can help developers identify and resolve issues quickly, improve performance, and optimize resource utilization. However, achieving these goals requires careful planning, implementation, and ongoing maintenance.

According to a survey by AppDynamics, 84% of organizations have experienced a failure in their applications in the last year, and the average cost of downtime is $5,600 per minute. In addition, a study by Gartner found that by 2023, 75% of large enterprises will have adopted a multi-cloud or hybrid IT strategy, increasing the complexity of application and infrastructure monitoring. These stats highlight the importance of effective monitoring and observability to prevent downtime and ensure optimal performance in today’s digital age.

In this blog, we will discuss the best practices for effective monitoring and observability.

1. Define your objectives and metrics:

To define your objectives and metrics, you need to understand what’s important for your application and business. For example, if you’re running an e-commerce website, you may want to track metrics such as the number of orders, revenue, and conversion rate. You can use tools like Google Analytics, Mixpanel, or Amplitude to track these metrics.


//Google Analytics code to track pageviews and events
<script async src=""></script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){dataLayer.push(arguments);}
  gtag('js', new Date());
  gtag('config', 'GA_TRACKING_ID');

2. Use the right tools

There are many monitoring and observability tools available, and choosing the right one depends on your requirements. For example, if you’re running a Kubernetes cluster, you may want to use tools like Prometheus, Grafana, and Fluentd to monitor your infrastructure and applications.


//Prometheus code to monitor Kubernetes cluster
kind: ServiceMonitor
  name: example-app
    app: example-app
      app: example-app
  - port: web
    path: /metrics
    interval: 15s

3. Monitor everything:

To monitor everything, you can use tools like Nagios or Zabbix, which can monitor your infrastructure, network, and applications.


//Nagios code to monitor network devices
define host {
  use     generic-switch
  host_name  switch1

define service {
  use     generic-service
  host_name  switch1
  service_description  Ping
  check_command    check_ping!100.0,20%!500.0,60%

define service {
  use     generic-service
  host_name  switch1
  service_description  SNMP Uptime
  check_command    check_snmp!-C public -o sysUpTime.0 -r 5 -m RFC1213-MIB

4. Automate as much as possible:

To automate monitoring tasks, you can use tools like Puppet, Ansible, or Chef, which can automate the deployment and configuration of monitoring tools.


//Puppet code to deploy and configure Prometheus
class { 'prometheus':
  version => '2.30.2',

prometheus::rule { 'disk_space':
  record => 'disk_space_available',
  expr   => 'node_filesystem_avail_bytes / node_filesystem_size_bytes',
  alert  => 'warning',

prometheus::alert { 'disk_space':
  expr     => 'disk_space_available < 0.2',
  for      => '1h',
  labels   => { severity => 'critical' },
  annotations => { summary => 'Disk space is running low' },

5. Monitor in real-time

To monitor in real-time, you can use tools like Datadog or New Relic, which can provide real-time insights into your applications and infrastructure.


//Datadog code to monitor real-time container metrics
apiVersion: v1
kind: ConfigMap
  name: datadog-agent
  datadog.yaml: |-
   - type: docker
        - name: DD_API_KEY
          value: YOUR_API_KEY_HERE
          value: "true"
          value: "true"
          value: "datadog-agent"
        - name: DD_APM_ENABLED
          value: "true"
        - name: DD_APM_NON_LOCAL_TRAFFIC
          value: "true"
          value: "true"
        - name: DD_CONTAINER_EXCLUDE
          value: "name:dd-agent, name:kube-proxy, name:istio-proxy"
        - name: DD_AC_INCLUDE
          value: "name:nginx, name:redis"
          value: "true"
          value: "false"
        - name: dockersock
          mountPath: /var/run/docker.sock
        - name: procdir
          mountPath: /host/proc
          readOnly: true
        - name: cgroups
          mountPath: /host/sys/fs/cgroup
          readOnly: true
      - name: dockersock
          path: /var/run/docker.sock
      - name: procdir
          path: /proc
      - name: cgroups
          path: /sys/fs/cgroup

6. Ensure scalability

As your applications and infrastructure grow, so too will the amount of data you need to monitor. Ensure that your monitoring and observability tools can scale to meet your needs. This includes ensuring that your infrastructure can support the data collection and analysis and that your tools can handle the increased workload.

7. Monitor user behavior

Monitoring user behavior is critical to understanding how your applications are being used and identifying issues before they become problems. Use tools that can track user behavior and identify patterns that may indicate issues with your application.

8. Collaborate

Effective monitoring and observability require collaboration between developers, operations teams, and other stakeholders. Make sure that all stakeholders have access to the data and insights they need to make informed decisions and work together to resolve issues quickly.

9. Review and Analyze data

Collecting data is only the first step. To get the most out of your monitoring and observability efforts, you need to review and analyze the data regularly. Use tools that can help you visualize and analyze the data, identify trends and patterns, and provide insights into performance and user behavior.

There are various tools available to help you visualize and analyze data, but one of the most popular tools is Grafana. Grafana is a free and open-source platform for data visualization, monitoring, and analysis.

To get started with Grafana, you need to first install it and configure it to connect to your data sources. Once you have done that, you can create dashboards that display your data in various formats, such as graphs, tables, and heatmaps.

Here’s an example of how to create a simple Grafana dashboard to visualize system metrics:

  • First, install and configure Grafana to connect to your data sources. You can follow the instructions on the Grafana website to do this.

  • Once you have installed Grafana and configured your data sources, log in to the Grafana web interface and create a new dashboard.

  • In the dashboard, add a new panel and select the type of visualization you want to use. For example, you can use a graph to visualize CPU usage over time.

  • Select the data source you want to use for the panel. For example, you can select your server monitoring tool as the data source.

  • Choose the metric you want to visualize. For example, you can choose the CPU usage metric.

  • Configure the panel settings to customize the visualization. For example, you can set the time range, add annotations, and adjust the graph style.

  • Save the panel and add more panels to the dashboard as needed.

Here’s an example of the code for a simple Grafana dashboard that displays CPU usage:

  "title": "Server Metrics",
  "panels": [
      "title": "CPU Usage",
      "type": "graph",
      "targets": [
          "query": "cpu.usage",
          "data source": "server-monitoring-tool"
      "time": {
        "from": "now-1h",
        "to": "now"
      "annotations": {
        "list": [
            "value": "Server rebooted",
            "time": "2023-03-20T13:30:00Z",
            "title": "Reboot"

10. Continuously Improve

Effective monitoring and observability are ongoing processes that require continuous improvement. Regularly review your monitoring and observability practices, and look for ways to optimize your processes, tools, and data collection.

To continuously improve, you can use tools like Grafana or Kibana to visualize your data and identify trends and patterns. You can also conduct post-incident reviews to identify areas for improvement.


//Grafana code to visualize application metrics
  "alias": "$tag_env - $tag_service",
  "bars": false,
  "datasource": "prometheus",
  "fill": 1,
  "id": 1,

11. Set up alerts and notifications:

To set up alerts and notifications, you can use tools like PagerDuty, OpsGenie, or VictorOps, which can send notifications via email, SMS, or chat.


//PagerDuty code to set up an alert for high CPU usage
  "routing_key": "YOUR_ROUTING_KEY",
  "event_action": "trigger",
  "payload": {
    "summary": "High CPU usage on server1",
    "source": "server1",
    "severity": "critical",
    "custom_details": {
      "cpu_usage": "95%"

12. Correlate data from different sources:

To correlate data from different sources, you can use tools like Splunk or ELK (Elasticsearch, Logstash, Kibana), which can aggregate and correlate data from different sources.


//ELK code to correlate data from different sources
input {
  beats {
    port => 5044

filter {
  if [fields][type] == "nginx-access" {
    grok {
      match => { "message" => "%{COMBINEDAPACHELOG}" }
    date {
      match => [ "timestamp", "dd/MMM/yyyy:HH:mm:ss Z" ]
    geoip {
      source => "clientip"

output {
  elasticsearch {
    hosts => ["elasticsearch:9200"]
    index => "%{[@metadata][beat]}-%{[@metadata][version]}-%{+YYYY.MM.dd}"

Effective monitoring and observability are critical for preventing downtime, optimizing performance, and ensuring the success of your business. By following best practices such as defining your objectives and metrics, using the right tools, monitoring everything, automating as much as possible, monitoring in real-time, and correlating data from different sources, you can gain real-time insights into your applications and infrastructure, and take proactive measures to ensure optimal performance and prevent failures.

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Karan Singh Karan is a highly experienced DevOps Engineer with over 13 years of experience in the IT industry. Throughout his career, he has developed a deep understanding of the principles of DevOps, including continuous integration and deployment, automated testing, and infrastructure as code.

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