What is Observability? Understanding Its Key Components and Benefits
Posted on February 12, 2025 • 25 min read • 5,224 wordsObservability is the art of understanding the performance of your systems by empowering you to get answers from the data produced by logs, metrics, and traces.
- Key Takeaways
- What Is Observability?
- How Observability Works
- Benefits of Observability
- Challenges in Achieving Observability
- Best Practices for Implementing Observability
- Differences Between Monitoring and Observability
- Importance of Observability for Businesses
- How to Make a System Observable
- Role of Open Source in Observability
- Getting Started with Observability
- Conclusion
- Frequently Asked Questions
Key Takeaways
- Observability is the key to full system performance and behavior insight. Using data from logs, metrics, and traces, you can quickly detect, troubleshoot, and solve problems in dynamic, complex cloud environments.
- Where traditional monitoring focuses on responding to problems once they are known, observability is all about proactively discovering the problems that we don’t yet know about. It enables better understanding of underlying systems health and environmental performance.
- Observability is made of three major pillars—logs, metrics, and traces—each serving an important purpose. All of these elements combine to inform you of the state of your system’s operations—your system’s operations should be crystal clear.
- Delivering on observability takes real-time data collection and analysis to unlock new efficiencies. It needs tools that readily integrate into current workflows, allowing for rapid identification of patterns, anomalies, and inefficiencies.
- Happy businesses are the ones that use observability to its full potential. It powers application performance, improves user experiences, fuels DevOps automation, and informs business-critical decisions with real-time insights.
- To implement observability successfully, organizations should align their goals with business priorities, leverage automation, foster collaboration between teams, and continuously refine their practices to adapt to evolving system needs.
The observability allows you to understand a system’s internal state. You do that by instrumentalizing your software and comprehensively analyzing the data it creates, from logs to metrics to traces. It empowers you to observe and understand complex systems, keeping them healthy and performant.
By emphasizing observable outputs, observability allows us to understand system behavior without needing to look under the hood of the actual system. This holistic approach is indispensable for troubleshooting performance bottlenecks, monitoring for anomalies, and ensuring the reliability of complex software applications.
Tools such as dashboards and alerting systems help with making sense of the data and enable a quicker response to emerging issues. Observability becomes even more critical in today’s complex distributed systems, where identifying the root cause of issues spread across dozens of services is nearly impossible without it.
It fosters improved decision-making and increased system health that leads to long-term success.
What Is Observability?
Observability is the art of understanding the performance of your systems by empowering you to get answers from the data produced by logs, metrics, and traces. Even more so as systems have migrated into complicated cloud ecosystems, making observability not just helpful, but essential. By enabling these monitoring elements through data-driven insights, observability enables teams to understand the underlying behavior of systems.
This allows them to fix problems faster and make proactive, preventative changes.
1. Definition of Observability
Observability is all about making sense out of data emitted by complex systems to determine their overall health and performance. It allows teams to query logs—time-stamped records of events such as failures, requests, and interactions—and metrics, which are time-stamped numeric representations of performance, for efficient troubleshooting.
When a web application is running a little slow, observability becomes your best friend. This makes it easy to pinpoint whether the issue lies with server response times, database queries, or network latency. In opposition to classic monitoring, observability explores the unknowns, providing insight into complex, distributed environments.
2. Key Components of Observability
The foundation of observability lies in three telemetry data types:
- Logs capture specific events or anomalies, providing granular details.
- Metrics are numerical aggregations, used to summarize performance trends (like CPU usage or number of requests per minute).
- Traces, which track requests across distributed systems and help visualize dependencies and bottlenecks.
Together, these components create a connected, collaborative approach, providing a complete picture of complex, interrelated systems. In cloud-native environments like Kubernetes, blending logs, metrics, and traces exposes the inefficient use of cloud resources, allowing you to optimize resource spending.
3. Differences from Traditional Monitoring
Traditional monitoring relies on static metrics and alerts but that approach doesn’t provide the depth required for today’s complex, dynamic systems. Observability, by contrast, combines and correlates data from all layers in real time to find root causes of problems.
For instance, if a database goes down, monitoring would alert you that the database is down, but observability would help you understand which upstream microservices are causing the issue. This curious, exploratory approach is key to what we call observability and arguably necessary in today’s complex environments of billions of interdependent components.
How Observability Works
Observability is about figuring out the internal state of a system by instrumenting it to collect data from its parts and studying that data to find answers. It gives IT and DevOps teams an unprecedented view into performance, how systems are acting, and where the problems may lie. By adopting this model, engineering teams can proactively validate systems in production, troubleshoot unforeseen issues, and deliver reliable experiences.
In practice, observability depends on the tools that aggregate and analyze performance data from applications, hardware, and networks to deliver actionable insights.
1. Collecting Data from Systems
This is why data collection is the first phase of observability. Logs, metrics, and traces are these three pillars, each providing their own, complementary view. Logs store rich, contextual records of events, and metrics offer important quantitative data, such as CPU load or memory usage.
Traces trace user requests through complex, distributed systems. It delivers real-time, actionable visibility into system health, allowing for rapid identification and remediation. Methods include:
- Log aggregation from servers and applications
- Metric collection through monitoring agents
- Trace analysis for distributed transaction tracking
2. Analyzing Logs, Metrics, and Traces
After the data is gathered, data analysis is the process that turns the data into useful insights and information. Through techniques like correlation and context analysis, we can connect logs, metrics, and traces, uncovering the relationships within our systems.
Such as a spike in latency on the dashboard matching up with error logs on another screen, indicating a single clear root cause. Tools such as Elastic Stack, Prometheus, or Jaeger make this analysis possible.
These platforms ingest massive datasets, leveraging machine learning to identify patterns and anomalies, revealing insights that may not be readily apparent to humans. Open instrumentation and AIOps tools further accelerate this process, adding more programmability and context at scale.
3. Identifying Patterns and Anomalies
Observability is really good at identifying patterns and anomalies. Measurable results allow teams to identify trends, like resource usage gradually increasing over time, or anomalies, such as a sudden spike in traffic.
Machine learning algorithms can amplify this capability. They sift through huge data sets to bring attention to nuanced discrepancies that may not be picked up as easily.
With anomaly detection, systems are more reliable because anomalies can be detected and addressed before they cause downtime, leading to optimal system performance.
Benefits of Observability
Observability offers extensive advantages in managing modern applications, ensuring systems are resilient, efficient, and aligned with business goals. By integrating this tool into their workflows, teams are empowered with more actionable insights. Consequently, they are better able to meet pressing challenges and make their systems work better.
1. Enhancing application performance
Those tools, called observability tools, are critical for understanding sudden performance bottlenecks or regressions. As an example, distributed tracing across microservices allows you to identify latency problems, so you know exactly what to optimize.
These tools improve resource allocation by identifying hardware/software usage patterns, making sure that any new hardware and software is used effectively. One large e-commerce platform achieved a 40% reduction in average page load times after deploying an observability solution. As a result, user churn dropped and revenue grew.
2. Improving end-user experiences
The user-centric insights offered by observability give you a clear picture of how your applications are performing in the real world. Monitoring tools identify problems, such as a sudden increase in response time, before the problem reaches the end users.
By taking advantage of this type of insight, one popular streaming service was able to make playback more reliable, increasing satisfaction among viewers.
3. Supporting DevOps and automation
Observability fits perfectly with the goals and practices of DevOps, enabling faster workflows by automatically detecting, diagnosing, and fixing incidents. Having observability integrated into pipelines with CI/CD ensures applications are performance compliant before release.
This minimizes downtime, improves workflows, and fosters a culture of continuous improvement.
4. Monitoring cloud and Kubernetes environments
Cloud-native, particularly Kubernetes-based systems, are even more challenging due to their inherently decentralized nature. Observability tools built for microservices architectures help deliver the right metrics, logs, and traces to observe complex, distributed systems.
Adhering to best practices, like centralizing data collection, guarantees full visibility to all environments.
5. Driving business insights
Observability helps connect the dots between technical performance and business results. With the immediate analytics provided from correlating the telemetry data to the KPIs, organizations have the intelligence to make smart, strategic decisions.
For instance, observability allowed a large retail organization to identify and optimize issues at the checkout, improving conversion rates by 25%.
Challenges in Achieving Observability
It is essential to understand, observe, and maintain the performance of increasingly complex systems. Doing so in an equitable manner is fraught with challenges. These challenges are a result of siloed data, large data sets, and the need to combine multiple tools. Overcoming these challenges takes a smart approach focused on maximizing efficiency, clarity, and collaboration.
1. Managing Data Silos
These data silos introduce deep barriers to observability. They silo information across multiple systems, complicating efforts to get a complete view of system performance. As an example, observability tools designed for web applications won’t necessarily be compatible with data from mobile or IoT environments.
This failure to integrate makes it easy to miss or overlook interdependencies between different digital channels. Addressing these silos requires centralizing data sources and leveraging a single observability platform. These platforms deliver a single pane of glass, allowing teams to compare trends and identify anomalies across all environments with ease.
2. Handling Data Complexity and Volume
New cloud-native environments, like AWS and Azure, are producing more telemetry data than ever before. In fact, CIOs say that 76% of them can’t get complete visibility because of this complexity. Making sense of raw data streams from complex, distributed systems can quickly become an insurmountable challenge without the right tools and techniques.
By working ahead to prioritize relevant information, such as structured logs containing rich metadata, organizations can make the analysis much easier. Advanced data management tools further simplify this process, ensuring that only the most actionable and valuable insights are extracted.
3. Avoiding Manual Configurations
Inefficiency and risk of human error are inherent in observability setups with manual configurations. Where legacy systems could get away with one-off configurations, today’s distributed systems require more nuanced solutions. Automated instrumentation tools take care of a lot of this setup automatically, speeding up deployment and minimizing the chances for human error.
Taking these best practices into account means investing in scalable solutions that adapt to future infrastructure needs while delivering high-quality, reliable data collection.
4. Troubleshooting Inefficiencies
Difficult to troubleshoot processes requiring multiple agency interactions and data sources slows incident resolution times. For instance, the ability to determine the root cause of performance issues spanning multiple clouds is virtually unattainable without centralized access to that data.
By bringing together data from multiple sources into a single view, observability tools help speed incident response, enabling teams to find and fix problems more quickly. Easy access to centralized data minimizes downtime and maximizes productivity.
5. Integrating Multiple Tools Effectively
Understandably, organizations put their trust in a variety of monitoring tools that are not interoperable. This poses an acute challenge in achieving observability, as it hampers understanding of system interdependencies or holistic application performance.
Strategies such as embracing open standards for data sharing and utilizing integration-friendly platforms are ways to close these gaps. A unified observability experience breaks down silos between Dev, Ops, and security teams and makes complex systems easier to manage.
Best Practices for Implementing Observability
Implementing observability successfully requires more than just deploying tools or gathering large amounts of data. It takes more than tools—it takes intentional strategies, informed by and serving the goals of the organization, focused on being ever-improving.
Observability goes beyond just monitoring, helping organizations establish an in-depth understanding of their system behavior so teams can quickly analyze and react to challenges. Here, we dig into what practices organizations should be following to make sure they’re implementing the right way.
1. Start with clear objectives
Setting clear success criteria is crucial to delivering value when rolling out observability. Organizations can start by defining and tracking measurable service-level objectives (SLOs) that matter to the business and offer a transparent structure for achieving success.
For instance, an organization may want to prioritize minimizing downtime or increasing response time for critical services. These objectives help inform what tools to adopt, what metrics to focus on, and make sure that observability initiatives are always intentional and relevant.
By aligning observability with broader organizational priorities, teams can demonstrate its value and integrate it into their daily workflows seamlessly.
2. Use continuous automation techniques
Not only does automating observability processes create a more efficient workflow, it improves scalability as well. With continuous automation, engineering teams can manage complex, large-scale systems that they simply cannot manually dive into every metric and every log detail.
Solutions such as automated alerting systems and machine learning algorithms are making it easier to spot patterns, detect anomalies, and simplify monitoring tasks. As an example, a retail platform might use automation and observability to track traffic surging in the busy shopping seasons and proactively maintain system stability.
These techniques improve both observability and efficiency, allowing organizations to respond and iterate quickly in the ever-changing world of cloud environments.
3. Leverage open-source solutions
Open-source observability tools combine flexibility with cost-effectiveness. Projects like Prometheus, Grafana, and Jaeger provide robust features for collecting and visualizing data while allowing teams to customize solutions to fit their needs.
These tools are all open-source and backed by passionate communities that promote innovation and establish best practices. For instance, Prometheus does a great job of collecting metrics, whereas Jaeger is a great option for tracing distributed systems.
When you adopt open-source solutions, you have access to the most cutting-edge capabilities without all that financial outlay.
4. Foster collaboration across teams
Cross-team collaboration and feedback is critical in your quest for meaningful observability. To better protect their organizations, development, operations, and security teams need to collaborate to eliminate silos and exchange knowledge.
Setting collective observability objectives fosters a collaborative spirit and makes sure everyone involved is on the same page regarding its significance. For example, a common dashboard showing system health metrics can increase transparency and encourage collaboration during incident response.
When teams come together to focus on the same goals, they’re better able to tackle problems and optimize the health of the entire system.
5. Embrace real-time monitoring tools
While developing new features quickly is important, real-time monitoring is just as crucial for keeping your system healthy and avoiding problems. Solutions such as Datadog and New Relic can give you on-the-fly visibility into new performance metrics, allowing you to address issues before they even occur.
With observability, a financial services company could monitor and act on every transaction in real-time. This allows them to proactively catch and fix latency issues before their customers ever experience them.
Proactive incident monitoring minimizes costly downtimes, improves the end user experience, and ensures the reliability of complex systems.
Differences Between Monitoring and Observability
Knowing the differences between monitoring and observability are key to successfully managing complex systems. Though both are important components of system reliability, the focus, approach, and the scope of insights provided by the two differs greatly.
1. Focus on Known vs Unknown Issues
Monitoring is a practice to measure specific metrics and known issues. For instance, it helps alert you when a server’s CPU usage suddenly spikes. It won’t tell you that spike is originating from a particular pod or container.
Conversely, observability goes further, revealing unforeseen issues through data exploration across metrics, logs, and traces. This level of visibility is priceless when responding to incidents in these modern, distributed systems, where 70% of IT teams say they face growing complexity.
By looking at data that goes deeper than pre-defined metrics, teams can find insights that help them tackle the unknown.
2. Reactive vs Proactive Approaches
Monitoring tends to be more reactive than observability, signaling an issue only after a threshold has been crossed. While observability may proactively uncover these potential issues, this allows teams to recognize and predict problems before they become disruptive.
This approach promotes better long-term management of our systems, leading to less prolonged downtime and better experiences for all users.
3. Scope of System Insights Provided
Monitoring gives a surface-level overview of what’s happening with your systems, such as server availability, or network health. Observability provides a holistic view, connecting what’s happening in the system to the underlying causes.
By providing this comprehensive perspective, observability helps teams make smarter decisions, leading to more predictable performance across complicated DevOps landscapes.
Importance of Observability for Businesses
At the end of the day, observability is about giving digital businesses a chance to succeed in today’s complex, fast-paced world. It does more than just monitor your systems to provide you with a complete view of system performance, user experience and business impact.
As businesses continue their adoption of cloud-native architectures, microservices, and distributed systems, businesses can’t afford to ignore observability. This is important for realizing operational excellence and fulfilling their strategic missions. Through observability, organizations are provided with powerful, actionable insights that lead to improved decision making, streamlined operations and enhanced customer experiences.
1. Understanding System Behavior Deeply
With observability, IT teams can gain a complete view of how their systems behave, allowing them to understand how various components work with each other. This knowledge is key to maximizing performance, because it provides the ability to pinpoint and address the worst bottlenecks or inefficiencies on our valuable infrastructure.
For instance, by visualizing data flows in a Kubernetes environment, teams can identify latency bottlenecks impacting containerized applications. Observability helps you get to the root cause of unexpected behavior by enabling real-time analysis of metrics, logs, and traces.
This deep level of understanding allows organizations to fix these troublesome issues before they become disruptive threats to business operations.
2. Enhancing Operational Efficiency
More efficient operations and less unplanned downtime are key results of observability. By enjoying real-time observability across their entire application stack, businesses can detect anomalies as they happen and fix them before the negative impact grows.
Observability is key to ensuring businesses are using their resources effectively, spotting where businesses have servers or services that are underutilized. Cloud environments such as AWS, Google, and Azure depend on observability.
It provides complete observability across your infrastructure that helps you optimize resource utilization. Higher system reliability means better uptime, a major factor in keeping service level agreements (SLAs) and keeping the trust of your end customers.
3. Optimizing User Satisfaction
With observability, organizations can react in real-time to user feedback, find root causes and mitigate issues to protect their customers’ experiences. As an example, application performance data analysis can uncover bottlenecks in checkout experience, enabling resolution in real-time before customers abandon their carts.
With observability, businesses have greater control to ensure their services are in line with what users expect, leading to improved user satisfaction and loyalty. Observability insights inform a company’s strategies to enable a program of continuous improvement.
All of these strategies enable organizations to get the most out of their products, providing frictionless experiences that drive customer retention and conversion.
How to Make a System Observable
Observability must be a foundational principle in system design. It gives developers the tools to understand what’s going on inside of a system at any point in time. Getting to observability takes intentionality, the strategic adoption of tools, and continual adjustment.
Here’s how to approach it effectively:
1. Design systems with observability in mind
Incorporating observability principles from the design phase on creates a system that is much more capable of providing actionable insights.
First, begin by instrumenting applications from the ground up. That means integrating metrics, logs, and traces natively within the codebase. For example, perhaps you add verbose logging around interfaces, such as API calls or database queries.
By structuring the architecture to improve observability, like making sure event-driven architectures or microservices are designed to be modular, it leads to more targeted, accurate monitoring. In doing so, you arm yourself with a solid foundation where problems can be spotted and addressed rapidly.
2. Integrate observability tools early
Bringing observability tools into the fold during the initial phases of development ensures that data collection, management, and analysis are all designed with intention.
Open source tools such as Prometheus, Grafana, or commercial options like DataDog can capture performance metrics and logs in near-real time. For instance, if you use observability tooling during the API development process, you can identify latency concerns prior to going live.
By integrating observability from the start, it becomes a core part of the system lifecycle that empowers teams to solve problems proactively, not just troubleshoot reactively.
3. Continuously refine instrumentation
Instrumentation is not static. It should be updated on a regular basis to keep up with a changing system and new priorities.
Systems of feedback, such as alerts and dashboards, are instrumental in identifying holes in the monitoring. They point out the lack of specific metrics in greater detail. A sudden increase in the error rate usually exposes some pretty opaque log output.
By iteratively improving your logging strategy, you can build up a more informative and clear context. Instrumenting for new features or larger-scaled components is the perfect time to make sure your observability measures are staying relevant and effective.
Role of Open Source in Observability
It’s hard to overstate how big a role open source plays in every aspect of modern observability practices today. It provides organizations with inclusive, flexible, and collaborative solutions to their monitoring and diagnostic requirements. By adopting open-source frameworks, businesses can enhance their existing observability systems.
Second, this approach fosters innovation and collaboration within the global tech ecosystem.
1. Benefits of Open-Source Observability Tools
The biggest benefit that open-source observability tools have is their flexibility and customization. Open-source tools enable developers to customize features and configurations to their specific needs, unlike proprietary solutions. OpenTelemetry provides a unified framework for gathering and handling telemetry data.
This makes it easy for organizations to integrate and correlate external data sources. This flexibility is especially useful for enterprises with intricate infrastructures.
Additionally, due to the community-driven nature of open-source projects, the pace of development for these tools is rapid. Open source communities of developers work together to continue to develop features, find bugs, and create usability improvements.
Projects such as Prometheus and Jaeger are perfect examples of how open-source projects can build robust, production-grade, scalable, monitoring and tracing solutions.
2. Encouraging Innovation and Collaboration
Open-source observability is a catalyst for collaboration and collective advancement. This collaboration leads to constant development of better tools and methodologies and a simpler development process. OpenTelemetry, for example, is backed by contributors from leading companies like Google, Microsoft, and Dynatrace, demonstrating how community involvement drives progress.
These partnerships empower many different and unique perspectives to inform tool development, helping to ensure solutions serve a wider array of needs. Trust—Open-source projects build trust through their transparency.
Organizations can see the underlying code and directly participate in its evolution. This collaborative endeavor not only pushes the boundaries of observability practices but ensures that access to the most advanced technologies is democratized.
3. Reducing Costs and Vendor Lock-In
Cost efficiency is a third, and perhaps the most compelling, reason to adopt open-source observability tools. Proprietary solutions tend to have high licensing costs with limited use cases. By comparison, open-source tools can save you up 70% or more in costs.
It’s a dramatic cost savings. By taking advantage of frameworks such as OpenTelemetry, organizations can build observability at scale without the costly observability tax.
Open-source tools are a critical part of avoiding vendor lock-in, allowing organizations to maintain flexibility in the long run. With open source, companies have more control over their observability stack, empowering them to adapt or replace components as their needs evolve.
This flexibility suits the constantly changing needs of today’s businesses, making open-source a logical long-term investment in observability.
Getting Started with Observability
Observability focuses on understanding the internal state of systems by examining their outputs. It’s an essential practice in modern IT environments, especially with rapid software delivery cycles driven by DevOps, continuous delivery, and agile development.
To implement observability effectively, organizations should follow a structured approach that assesses current systems, selects appropriate tools, and equips teams with the necessary knowledge and skills.
1. Assess current system capabilities
Evaluating existing systems is a vital first step. This includes analyzing metrics, events, logs, and traces (MELT) to understand the system’s readiness for observability.
Identifying strengths, such as well-instrumented applications, and limitations, like gaps in data coverage, helps shape strategies. For instance, knowing which components lack visibility can guide targeted improvements.
This evaluation clarifies relationships and interdependencies, revealing how billions of interconnected components perform as a whole. Clear insights enable organizations to align observability with their service-level objectives (SLOs).
2. Choose the right tools and strategies
Choosing the right tools for your organization’s specific needs helps you stay focused and makes your observability efforts more effective. For example, tools need to match desired outcomes, be it diagnosing slowdowns or preventing downtime.
Strategic planning is huge. Strategic planning to integrate observability tools into workflows and foster proactive system-wide monitoring will go a long way.
Lastly, it’s important to remember that observability is an addition to, not a replacement of, traditional monitoring. Investment with a well-chosen toolset can yield a ridiculously high ROI.
Surprisingly, 58% of organizations say they’ve already realized more than $5 million in annual benefits from their observability investments.
3. Train teams on observability practices
Training helps teams get the most out of observability tools and concepts. From MELT fundamentals to industry best practices, we’re here to help you integrate MELT across workflows.
Prioritizing a culture of observability sparks cross-team collaboration. Dev and Ops teams work together to achieve SLOs and keep applications performing at their best.
Regular training helps teams stay informed on new trends and advances, making them better equipped to tackle new, complicated system behaviors. A well-trained team is the best guarantee that observability grows into a core organizational capability.
Conclusion
Observability provides systems with the transparency that enables them to operate in a reliable and efficient manner. It enables teams to identify issues more quickly, gain a deeper insight into their systems, and optimize performance continuously. By focusing on the right practices and tools, you can build healthy systems. These systems will be inflexible and incapable of adapting to change or scaling up. Open source options remove a lot of friction from the getting-started process. You can take the plunge without breaking the bank or becoming locked in to one proprietary vendor.
The road to observability won’t happen overnight, but the positive impact it has on your business and operations will be well worth the investment. The advice we can give is to start small, be persistent, and continue to educate yourself. The more advanced you get, the more your systems will work for you. Observability isn’t just a technology—it’s a culture that fosters better decision-making from the ground up.
Frequently Asked Questions
What is observability?
Observability is what allows you to understand the true, internal state of a system. The way you get there is by enriching its outputs with observability – logs, metrics, and traces. It’s proven to help IT teams detect, troubleshoot, and resolve issues faster, resulting in more seamless operations.
How does observability differ from monitoring?
Monitoring is the practice of tracking a set of predefined metrics to identify and alert on known issues. Observability is a step further, enabling teams to understand unexpected problems through the exploration of their systems’ data. It’s less about figuring out what happened and more about why something happened.
Why is observability important for businesses?
Heralded as essential to the reliability of today’s complex systems, observability increases uptime and contributes to a better customer experience. It allows companies to proactively monitor their applications and services, rapidly detect and resolve issues, and maintain smooth business operations while improving end-user experience.
What are the main benefits of observability?
The primary benefits are the ability to resolve issues more quickly, increased performance of systems, improved uptime, and better collaboration between teams. Beyond that, it gives you more thorough understanding of system behavior, so you can solve issues before they impact users.
What challenges come with achieving observability?
These challenges might involve managing massive amounts of data, tool integration, and making sure all members of your team are observability experts. Without the proper strategy, teams will find it difficult to pull actionable insights from their systems.
How can a system become observable?
To achieve observability in any system, invest in instrumentation like logging, metrics, and tracing. Find solutions that aggregate and correlate this information. Make sure the design of the system provides you with insight into what the system is doing under the hood.
What role does open source play in observability?
Open-source tools, such as Prometheus and Grafana, offer both a cost-effective and highly flexible observability solution. They let companies build their own observability stack, but still get the scale, support, and innovation of the community at large.