Category: Observability & Monitoring
CNCF Maturity Level: Graduated
Maintainers: CNCF community
GitHub: prometheus/prometheus
Prometheus is a powerful open-source monitoring and alerting toolkit designed for reliability and scalability in cloud-native environments. Originally developed at SoundCloud, it became the second project to join the CNCF, after Kubernetes, and has since become the go-to solution for time-series monitoring.
Prometheus excels at collecting metrics, storing them efficiently, and providing rich query capabilities using its native PromQL query language.
In distributed systems like Kubernetes, traditional monitoring tools fall short. Prometheus offers:
Prometheus is battle-tested in production environments and supports millions of metrics with minimal overhead.
Understanding Prometheus architecture helps you build robust observability pipelines:
Prometheus complements Kubernetes perfectly and is part of most CNCF observability stacks. It’s used alongside:
Prometheus is used at scale by:
You can deploy Prometheus using the kube-prometheus-stack Helm chart:
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install monitoring prometheus-community/kube-prometheus-stack
Access the Prometheus UI:
kubectl port-forward svc/monitoring-kube-prometheus-prometheus 9090
Explore metrics like:
up
node_cpu_seconds_total
kube_pod_container_status_restarts_total
Pros:
Cons:
Prometheus is the cornerstone of observability in cloud-native environments. With first-class Kubernetes support, flexible querying, and wide adoption, it’s a must-have for any DevOps, SRE, or platform engineering team.
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Category: Orchestration & Management
CNCF Maturity Level: Graduated
Maintainers: Originally developed by Google, now maintained by the CNCF
GitHub: kubernetes/kubernetes
Kubernetes (often abbreviated as K8s) is the leading open-source platform for automating the deployment, scaling, and management of containerized applications. It serves as the operating system for cloud-native applications, allowing teams to deploy complex, distributed systems reliably.
Born at Google and inspired by their internal system Borg, Kubernetes was open-sourced in 2014 and is now a graduated project under the Cloud Native Computing Foundation (CNCF).
Kubernetes simplifies the operational overhead of managing containers, making it the cornerstone of modern DevOps workflows. It supports:
Understanding Kubernetes architecture helps unlock its true power. It consists of a control plane and worker nodes:
Kubernetes is the linchpin of the CNCF landscape, influencing nearly every other project. Many tools integrate directly with Kubernetes for monitoring, security, service mesh, logging, and storage. Projects like:
These tools build on Kubernetes to offer powerful, scalable infrastructure solutions.
Kubernetes supports production workloads at scale:
You can try Kubernetes locally using kind:
# Prerequisites: Install Docker, kind, and kubectl
kind create cluster --name demo
kubectl get nodes
kubectl create deployment hello --image=nginx
kubectl expose deployment hello --port=80 --type=NodePort
kubectl get svc
Prefer a managed solution? Check out:
Pros:
Cons:
Kubernetes is more than a buzzword it’s a powerful platform that has redefined how software runs at scale. Whether you’re deploying microservices, building ML pipelines, or managing hybrid infrastructure, Kubernetes provides the tools and abstraction you need.
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Deploying applications in Kubernetes (K8s) environments adds complexity to observability due to the distributed nature of microservices and dynamic infrastructure. OpenTelemetry (OTel), combined with the OpenTelemetry Collector deployed in Kubernetes, provides a powerful and flexible observability pipeline for collecting, processing, and exporting traces, metrics, and logs.
This guide walks you through setting up OpenTelemetry in your application to send telemetry data to an OpenTelemetry Collector running in Kubernetes.
Before you begin, ensure you have:
The OpenTelemetry Collector acts as an agent or gateway to receive telemetry data and export it to backends like Jaeger, Prometheus, or Datadog.
helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm repo update
helm install otel-collector open-telemetry/opentelemetry-collector -n observability --create-namespace
kubectl get pods -n observability
Create a otel-collector-config.yaml for the Collector configuration:
receivers:
otlp:
protocols:
grpc:
http:
exporters:
logging:
loglevel: debug
jaeger:
endpoint: "jaeger-collector:14250"
tls:
insecure: true
service:
pipelines:
traces:
receivers: [otlp]
exporters: [logging, jaeger]
Deploy the Collector:
kubectl apply -f otel-collector-config.yaml
Create a Kubernetes Service to expose the Collector for your application to send data.
apiVersion: v1
kind: Service
metadata:
name: otel-collector
namespace: observability
spec:
ports:
- name: grpc
port: 4317
targetPort: 4317
- name: http
port: 4318
targetPort: 4318
selector:
app.kubernetes.io/name: opentelemetry-collector
Apply the service:
kubectl apply -f otel-collector-service.yaml
Your application needs to send telemetry data to the exposed otel-collector service using the OTLP protocol.
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
otlp_exporter = OTLPSpanExporter(endpoint="otel-collector.observability.svc.cluster.local:4317")
trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(otlp_exporter))
with tracer.start_as_current_span("example-span"):
print("Sending trace to OpenTelemetry Collector in Kubernetes")
const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-grpc');
const { BatchSpanProcessor } = require('@opentelemetry/sdk-trace-base');
const provider = new NodeTracerProvider();
const exporter = new OTLPTraceExporter({ url: 'grpc://otel-collector.observability.svc.cluster.local:4317' });
provider.addSpanProcessor(new BatchSpanProcessor(exporter));
provider.register();
const tracer = provider.getTracer('example-service');
const span = tracer.startSpan('example-span');
console.log('Trace sent to Collector');
span.end();
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.api.trace.Tracer;
import io.opentelemetry.sdk.trace.SdkTracerProvider;
import io.opentelemetry.exporter.otlp.trace.OtlpGrpcSpanExporter;
import io.opentelemetry.sdk.trace.export.BatchSpanProcessor;
SdkTracerProvider tracerProvider = SdkTracerProvider.builder()
.addSpanProcessor(BatchSpanProcessor.builder(OtlpGrpcSpanExporter.builder()
.setEndpoint("otel-collector.observability.svc.cluster.local:4317")
.build())
.build())
.build();
Tracer tracer = tracerProvider.get("example-service");
Span span = tracer.spanBuilder("example-span").startSpan();
System.out.println("Sending trace to OpenTelemetry Collector");
span.end();
Deploy backends like Jaeger for tracing and Prometheus for metrics.
kubectl create namespace tracing
kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/master/deploy/examples/simplest.yaml
Access Jaeger UI:
kubectl port-forward svc/jaeger-query 16686:16686 -n tracing
Visit: http://localhost:16686
helm install prometheus prometheus-community/prometheus -n monitoring --create-namespace
Access Prometheus UI:
kubectl port-forward svc/prometheus-server 9090 -n monitoring
Visit: http://localhost:9090
Once your application and the Collector are running:
✅ Tip: Use Grafana to create dashboards combining metrics and traces for comprehensive observability.
Setting up OpenTelemetry to send data from your application to the OpenTelemetry Collector in Kubernetes provides scalable and flexible observability. By following these steps, you gain real-time insights into your application’s performance, enabling faster debugging, improved reliability, and better end-user experiences.
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In today’s fast-paced world of software development, ensuring that applications are reliable, performant, and scalable is more important than ever. With the rise of microservices, cloud-native architectures, and distributed systems, understanding how different components of an application interact has become increasingly complex. This is where OpenTelemetry (OTel) comes in.
OpenTelemetry is an open-source observability framework that enables developers to collect and analyze traces, metrics, and logs—the three fundamental data types needed to understand application behavior. But why should developers, not just DevOps or SRE teams, care about OpenTelemetry? This article breaks down the key reasons.
Nothing is more frustrating for developers than spending hours trying to locate the root cause of a production issue. Traditional debugging methods, like sifting through endless log files, are inefficient in distributed environments.
🔍 Example: If a user reports slow checkout times, OpenTelemetry traces can reveal exactly where the delay occurs—whether it’s the frontend API call, payment service, or database query.
Developers strive to write efficient code, but without proper observability, it’s difficult to understand how code changes impact the overall system.
🚦 Pro Tip: Use OpenTelemetry’s auto-instrumentation to gather insights with minimal code changes while manually instrumenting critical code paths for deeper analysis.
Observability isn’t just for DevOps; it’s a shared responsibility across development, operations, and security teams. OpenTelemetry’s standardized data collection bridges the communication gap.
🤝 Scenario: During a production outage, developers can quickly provide trace data to SREs, significantly reducing mean time to resolution (MTTR).
Relying on proprietary monitoring tools can lead to vendor lock-in, making it expensive and cumbersome to switch platforms later.
🌍 Bonus: OpenTelemetry is part of the Cloud Native Computing Foundation (CNCF), ensuring long-term support and stability.
Ultimately, observability isn’t just about system health—it’s about the end-user experience. Slow-loading pages or failed transactions can drive users away.
💡 Real-World Example: E-commerce platforms using OpenTelemetry have reduced cart abandonment rates by identifying and fixing slow checkout processes.
Developers don’t need to overhaul their entire stack to benefit from OpenTelemetry. It’s designed to integrate seamlessly with popular frameworks and libraries.
🛠️ Quick Start:
pip install opentelemetry-sdk
opentelemetry-instrument python app.py
🔗 Result: Collect traces and metrics without major code refactoring.
With CI/CD pipelines, microservices, and serverless architectures becoming the norm, developers who understand observability gain a significant career advantage.
🎯 Career Boost: Knowledge of OpenTelemetry and observability tools is increasingly in demand among top tech employers.
Developers can no longer afford to ignore observability. OpenTelemetry provides a powerful, open-source solution that helps developers debug faster, write better code, and collaborate more effectively—all while improving the end-user experience.
By adopting OpenTelemetry, you’re not just adding another tool to your stack; you’re embracing a smarter, data-driven approach to software development that benefits your code, your team, and your users.
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In modern, distributed systems, maintaining reliability, performance, and scalability requires deep visibility into application behavior. Observability provides this visibility, allowing teams to detect, investigate, and resolve issues efficiently. Traditionally, observability is built upon three fundamental data types—commonly referred to as the Three Pillars of Observability: logs, metrics, and traces.
OpenTelemetry (OTel), as a leading open-source observability framework, unifies the collection and correlation of these three pillars, offering a comprehensive solution to monitor and understand complex systems.
This article explores the three pillars of observability in OpenTelemetry, their significance, and how they work together to provide end-to-end visibility.
Observability relies on three primary telemetry data types:
Logs are timestamped, textual records that capture discrete events within a system. They can include debug messages, error reports, transaction details, and custom application events.
OpenTelemetry supports structured logging with context propagation. This allows logs to be linked to traces, offering deeper insight into where issues occur within a distributed request flow.
exporters:
logging:
loglevel: debug
🔎 Example: When an API call fails, logs provide the error message, request payload, and stack trace, helping engineers quickly pinpoint the issue.
Metrics are numerical representations of data measured over intervals of time. They provide a quantitative view of system health and performance.
OpenTelemetry enables automatic and manual metric collection, supporting counters, gauges, and histograms.
receivers:
otlp:
protocols:
http:
exporters:
prometheus:
endpoint: "0.0.0.0:8889"
📈 Example: Metrics can show the average response time of a service, alerting teams if latency exceeds acceptable limits.
Traces represent the journey of a request through various components of a distributed system. A trace is composed of spans, each detailing an individual operation.
OpenTelemetry supports automatic instrumentation across multiple languages and frameworks, making it easier to capture and visualize trace data.
receivers:
otlp:
protocols:
grpc:
exporters:
jaeger:
endpoint: "localhost:14250"
🌐 Example: Traces can reveal that a delay in user checkout is due to a slow database query within the payment service.
While logs, metrics, and traces each offer valuable insights on their own, their true power emerges when used together. OpenTelemetry facilitates this correlation:
✅ Scenario: A sudden spike in response time metrics triggers an alert. Using traces, you identify which service in the request flow causes the delay. Logs from that service reveal a recent configuration change, pinpointing the root cause.
🔄 Benefits of Unified Observability:
| Aspect | Traditional Monitoring | OpenTelemetry Approach | |———————–|———————————————–|———————————————-| | Data Correlation | Siloed data sources with manual correlation. | Automatic correlation across all three pillars. | | Vendor Lock-In | Often tied to proprietary solutions. | Open, vendor-neutral standard. | | Ease of Integration | Requires multiple tools and configurations. | Single framework with comprehensive support.| | Context Propagation | Limited or non-existent. | End-to-end context propagation enabled. |
✅ 1. Start with Auto-Instrumentation: Quickly gather telemetry without extensive code changes.
✅ 2. Use the OpenTelemetry Collector: Centralize data collection, processing, and exporting.
✅ 3. Correlate Logs with Traces: Ensure logs include trace context to enhance troubleshooting.
✅ 4. Set Meaningful Metrics: Focus on service-level indicators (SLIs) relevant to your business goals.
✅ 5. Continuously Review Telemetry Data: Use data insights to improve system resilience and user experience.
The Three Pillars of Observability—logs, metrics, and traces—are essential for understanding and maintaining modern, distributed systems. OpenTelemetry provides a unified framework to collect, correlate, and analyze these data types, offering comprehensive observability that surpasses traditional methods.
By leveraging OpenTelemetry, organizations can proactively detect issues, reduce downtime, and improve application performance. Whether you’re just starting with observability or enhancing existing practices, embracing the three pillars with OpenTelemetry is a step toward more reliable and efficient systems.
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Modern applications, especially those built with microservices and cloud-native architectures, require comprehensive visibility to maintain reliability and performance. Traditional monitoring solutions served this purpose for monolithic systems, but they often fall short in complex, distributed environments. OpenTelemetry (OTel) addresses these challenges by offering unified observability across traces, metrics, and logs.
In this article, we’ll explore the key differences between OpenTelemetry and traditional monitoring solutions, highlighting how OpenTelemetry meets the demands of modern applications.
Traditional monitoring focuses on tracking predefined metrics such as CPU usage, memory consumption, and request counts. These solutions often rely on host-based agents and provide dashboards with alerts for performance thresholds.
OpenTelemetry is an open-source observability framework that collects traces, metrics, and logs in a standardized, vendor-agnostic way. Unlike traditional monitoring, OpenTelemetry provides end-to-end visibility into application performance, especially in distributed environments.
| Feature | Traditional Monitoring | OpenTelemetry | |——————————–|———————————————–|———————————————–| | Observability Coverage | Focuses mainly on metrics and basic logs. | Unified traces, metrics, and logs. | | Distributed Tracing | Limited or absent. | Built-in with full context propagation. | | Data Correlation | Metrics, logs, and traces are siloed. | Correlates all telemetry data seamlessly. | | Vendor Lock-In | High (proprietary protocols and tools). | Low (open standards and multiple exporters). | | Scalability | Struggles in microservices environments. | Designed for modern, cloud-native systems. | | Auto-Instrumentation | Limited support. | Extensive support across languages & frameworks. | | Real-Time Insights | Basic alerts on static thresholds. | Dynamic insights with trace-context linkage. | | Cost Efficiency | High with proprietary tools. | Cost-effective with open-source options. |
Traditional monitoring tools can’t track requests across microservices. OpenTelemetry’s distributed tracing captures the journey of each request, revealing latency bottlenecks and service dependencies.
Instead of juggling separate tools for metrics, logs, and traces, OpenTelemetry provides a single framework to collect and correlate all telemetry data.
OpenTelemetry’s vendor-neutral approach lets you switch between observability backends like Grafana, Prometheus, or Datadog without changing instrumentation code.
Cloud-native platforms like Kubernetes and serverless environments are complex. OpenTelemetry’s support for these systems enables more effective monitoring compared to traditional solutions.
As part of the Cloud Native Computing Foundation (CNCF), OpenTelemetry benefits from an active community, continuous updates, and broad ecosystem support.
| Monitoring Need | Traditional Monitoring Approach | OpenTelemetry Approach | |—————————–|———————————————–|————————————————| | Latency Bottlenecks | Requires manual log analysis. | Traces highlight slow services instantly. | | Error Diagnosis | Separate tools for logs and metrics. | Unified data pinpoints root cause quickly. | | Scaling Infrastructure | Metrics show resource usage but lack context. | Metrics + traces show impact on user requests. | | Deployment Changes | Limited visibility on deployment impacts. | Can correlate deployments with performance. |
While OpenTelemetry excels in modern environments, traditional monitoring tools may still be suitable when:
Traditional monitoring provides basic insights suitable for simpler architectures, but it struggles with the complexity of distributed systems. In contrast, OpenTelemetry offers a comprehensive, scalable, and vendor-neutral approach tailored for modern applications.
By adopting OpenTelemetry, teams gain full visibility into their applications, streamline troubleshooting, and make informed decisions based on unified observability data.
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In the world of modern software development, observability is crucial for maintaining reliable and efficient applications. With distributed systems, microservices, and cloud-native architectures becoming the norm, developers need a powerful tool to monitor and analyze their applications effectively. OpenTelemetry has emerged as the leading open-source observability framework, enabling developers to collect, process, and export telemetry data (logs, metrics, and traces) in a standardized way.
This guide will introduce OpenTelemetry, explain its components, and highlight why it’s essential for modern software development.
OpenTelemetry (OTel) is an open-source observability framework that provides a unified approach to collecting telemetry data (logs, metrics, and traces) from applications and infrastructure. It standardizes how telemetry data is generated, processed, and sent to observability backends like Prometheus, Grafana, Jaeger, Datadog, and others.
OpenTelemetry is part of the Cloud Native Computing Foundation (CNCF) and is designed to help developers and DevOps teams gain deep insights into their applications’ performance and behavior.
Traditional monitoring solutions often use separate tools for logging, metrics, and tracing, leading to data silos and inefficiencies. OpenTelemetry addresses this by:
OpenTelemetry consists of several key components that work together to collect and process telemetry data:
Instrumentation is the process of adding observability code to your application. OpenTelemetry provides SDKs, APIs, and auto-instrumentation libraries to make this process easy.
OpenTelemetry supports multiple programming languages and frameworks for instrumentation. Here are examples for popular ones:
pip install opentelemetry-sdk opentelemetry-auto-instrumentation
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("sample-span"):
print("Hello, OpenTelemetry!")
npm install --save @opentelemetry/api @opentelemetry/node
const opentelemetry = require("@opentelemetry/api");
const tracer = opentelemetry.trace.getTracer("example-tracer");
const span = tracer.startSpan("sample-span");
console.log("Hello, OpenTelemetry!");
span.end();
<dependency>
<groupId>io.opentelemetry</groupId>
<artifactId>opentelemetry-api</artifactId>
<version>1.0.0</version>
</dependency>
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.api.trace.Tracer;
public class OpenTelemetryExample {
private static final Tracer tracer =
io.opentelemetry.api.GlobalOpenTelemetry.getTracer("example");
public static void main(String[] args) {
Span span = tracer.spanBuilder("sample-span").startSpan();
System.out.println("Hello, OpenTelemetry!");
span.end();
}
}
go get go.opentelemetry.io/otel
package main
import (
"context"
"fmt"
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/trace"
)
func main() {
tracer := otel.Tracer("example-tracer")
ctx, span := tracer.Start(context.Background(), "sample-span")
defer span.End()
fmt.Println("Hello, OpenTelemetry!")
}
dotnet add package OpenTelemetry
using System;
using OpenTelemetry.Trace;
class Program
{
static void Main()
{
var tracerProvider = Sdk.CreateTracerProviderBuilder().AddSource("example").Build();
var tracer = tracerProvider.GetTracer("example");
using (var span = tracer.StartActiveSpan("sample-span"))
{
Console.WriteLine("Hello, OpenTelemetry!");
}
}
}
Traces help track the journey of a request as it moves through different services. Each request is broken down into spans, representing individual operations.
Metrics capture numerical data about system performance, such as CPU usage, memory consumption, request latency, and error rates. OpenTelemetry supports various metric types, including gauges, counters, and histograms.
Logs provide detailed event data and debugging information. OpenTelemetry enables structured logging and correlates logs with traces and metrics for a comprehensive view of system behavior.
The OpenTelemetry Collector is an optional but powerful component that receives, processes, and exports telemetry data to observability backends. It helps with data aggregation, filtering, batching, and security.
Exporters send collected telemetry data to various observability platforms like Grafana, Jaeger, Prometheus, New Relic, and Datadog. OpenTelemetry supports multiple exporters, allowing seamless integration with existing monitoring tools.
OpenTelemetry simplifies observability by standardizing how applications collect and process telemetry data. Whether you’re working with microservices, cloud-native applications, or traditional architectures, OpenTelemetry helps gain deeper insights into system performance, improving reliability and troubleshooting.
By adopting OpenTelemetry, teams can make data-driven decisions, enhance application monitoring, and reduce downtime, ensuring a seamless user experience. If you haven’t explored OpenTelemetry yet, now is the perfect time to start!
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In the previous introductory article we looked at the basics of observability and its pillars that contribute to the platform. In this and the some of the next articles, lets look at them in bit detail. Let’s look at the role of Metrics in Observability.
Metrics form the foundation of observability by offering quantitative insights into the inner workings of complex systems, enabling teams to effectively monitor, troubleshoot, and optimize their performance. It plays a crucial role in the understanding various aspects of systems such as system health, resource utilization, error rates, throughput, latency, and many other key indicators.
In an observability platform, metrics contributes with,
While metrics play a crucial role in observability by providing quantitative data about the performance and behavior of a system, effective use of metrics in an observability platform involves some best practices to ensure that the data collected is meaningful, actionable, and scalable,
By adhering to these best practices, organizations can leverage metrics effectively within their observability platforms to gain actionable insights, improve system reliability, and drive business value. We will look at some of the strategies for getting the metrics and challenges associated with it in the next article.
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Observability is a fundamental concept in various fields such as engineering, computer science, and systems theory. It refers to the ability to understand the internal state of a system solely through its outputs, allowing for effective monitoring, troubleshooting, and optimization. In complex systems, especially those involving software, networks, or large-scale infrastructure, observability plays a crucial role in ensuring reliability, performance, and maintainability.
In the context of modern software development and system architecture, observability has become increasingly important as systems have grown more complex and distributed. The ability to observe and understand the behaviour of these systems in real-time is critical for ensuring their reliability, diagnosing issues, and optimizing performance.
Following are the primary components to observability platform:
Metrics: Metrics are quantitative measurements that provide insights into the performance, health, and behaviour of a system. These could include things like response times, error rates, CPU usage, or any other relevant data points that help us understand how the system is performing.
Logs: Logs are records of events or activities that occur within a system. They provide a detailed chronological history of what has happened, including any errors or exceptions that occurred, user actions, system events, and more. Logs are invaluable for troubleshooting issues and understanding the sequence of events that led to a particular outcome.
Traces: Traces are records of the flow of a specific request or transaction as it moves through a distributed system. They capture information about how the request traverses different components or services, including any network calls, database queries, or other interactions. Traces help us understand the end-to-end behaviour of a system and identify performance bottlenecks or issues with specific components.
Events: Most modern distributed systems have the capability to generate events either themselves or with the help of orchestrator / cloud platform. This helps to create a trail or feed into other modules like Alerting or create custom metrics.
Alerts: Monitoring all of your infrastructure in one place wouldn’t be complete without the ability to know when critical changes are occurring. Having an alerting mechanism to create the alerts and send notifications is an important part of the observability platform.
To achieve observability, systems need to be properly instrumented with tools and techniques that collect and analyze metrics, logs, and traces. This often involves incorporating monitoring tools, logging frameworks, distributed tracing systems, and other observability solutions into the architecture of the system. By embracing observability principles and investing in the right tools and practices, organizations can gain valuable insights into the behaviour of their systems, enabling them to detect and diagnose issues quickly, optimize performance, and ultimately deliver better experiences for their users.
Next in the series is the role of metrics in the observability platform.
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