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What is Monitoring? What is Observability? and How different between monitoring and observability ?

Certainly! Both observability and monitoring are widely discussed in the IT industry. I believe many people, including myself, have encountered these terms. Now, do you know the actual differences between observability and monitoring? When facing issues in an application, it's crucial to accurately identify the problem or root cause. Does anyone know the real difference between observability and monitoring?

The key when problems arise in an application is being able to pinpoint the true issue or the root cause. This is essential for making accurate fixes. If not addressed promptly, it can adversely impact customer experience, and encountering such unfortunate situations is inevitable. It can significantly affect your business reputation.

Monitoring helps you be aware of the current state of affairs, while Observability informs you about what is happening and what actions you should take in a given situation.

Let's delve deeper into understanding the differences between observability and monitoring before exploring how you can leverage both for the benefit of your business outcomes.

Certainly! Let's delve into the meanings of Monitoring and Observability. What is Monitoring ?

Monitoring, by definition, is the process of collecting, analyzing, and utilizing data to track the progress of a system towards its objectives. It is a decision-making framework that emphasizes the continuous watch over specific metrics. While logs provide additional information, monitoring generally focuses on observing metrics within the context of the system.

What is Observability ?

Observability is the capability to understand the internal state of a system by analyzing the data the system generates, such as logs, metrics, and traces. The power of observability lies in its ability to help teams analyze and understand what is happening in complex, multi-cloud environments. It enables the detection and resolution of issues by providing insights into the system's behavior. Now that we have defined each term, let's explore the differences between Observability and Monitoring for a better understanding. The difference between Observability and Monitoring lies in their nature and focus. Monitoring is the process of collecting and displaying data, while Observability involves the ability to discern the system's state through analysis of incoming and outgoing data.

For example:

In monitoring, we can track metrics that undergo changes and visualize if there are any issues. For instance, checking a metric for a spike in CPU usage or a sudden increase in response time. This is a monitoring activity.

Observability: Observability, on the other hand, goes beyond mere data collection. It involves generating valuable insights into the internal state of the system. If we create data that is meaningful for understanding the system's internal state or is crucial in determining the root cause of an issue, it falls under observability. In summary, monitoring focuses on tracking specific metrics for changes, while observability is about gaining a deeper understanding of the system's behavior through meaningful analysis of data.

Above, we've discussed the differences.

Now, let's explore the similarities between Observability and Monitoring.

Both Observability and Monitoring share similar concepts and objectives, primarily providing in-depth insights into the performance and operations of a system. They leverage techniques such as data collection, analysis, and visualization to carry out proactive detection and troubleshooting of issues. Ultimately, these practices aim to equip engineers with the confidence in the system's reliability, enhance operational efficiency, and optimize resource utilization.

When it comes to choosing between Observability and Monitoring, determining which is better depends on the specific needs and characteristics of your environment. In general, Monitoring offers a limited view of data, often focusing on key metrics. This approach helps understand why a system might fail, with a particular emphasis on critical metrics such as utilization rates or incoming workload volume. Monitoring provides an overall performance perspective, such as when monitoring a database, where you may want insights into write latency or query responsiveness. Experienced database administrators can identify common patterns that may indicate frequent issues, such as increasing memory usage, decreasing cache hit ratios, or rising CPU utilization. These issues might highlight poorly optimized queries that need attention and correction. In contrast, Observability takes a broader approach by incorporating various data sources like metrics, logs, and traces. This comprehensive view enables a deeper understanding of the system's behavior, facilitating the detection and resolution of complex issues. Observability is particularly valuable in dynamic and distributed environments, offering insights beyond what traditional Monitoring may provide. To determine which approach is most suitable for your environment, consider the complexity of your system, the need for detailed insights, and the potential challenges you may encounter. Evaluating specific use cases and understanding the depth of visibility required will guide you in choosing the most effective model for your monitoring and diagnostic needs.

Analyzing the performance of a general database is relatively straightforward compared to a microservices architecture with multiple components and numerous dependencies. Monitoring becomes highly beneficial when understanding how a system fails in such complex scenarios. As applications become more intricate, so do the challenges associated with their failures. Often, predicting how a microservices architecture may fail can be challenging. However, with an Observability approach, you can gain insights into the internal state of the system and understand what is not working and why.

In modern and complex applications, the relationship between a few metrics may not be sufficient to diagnose events effectively. Nevertheless, contemporary and sophisticated applications require increased system visibility. This can be achieved by combining the capabilities of Observability and Monitoring tools, allowing for a more powerful and comprehensive approach to obtain meaningful results. By integrating these tools, you can enhance your ability to observe and understand the status of your system, enabling you to pinpoint issues and diagnose failures more effectively in contemporary, complex, and dynamic environments.

3 pillars of Observability are Logs, Metrics, and Traces.

As mentioned earlier, Observability is the understanding of what is happening in a system based on existing data, including Logging, Metrics, and Tracing. A system is considered observable when data of these types is generated and made available, enabling you to assess the system's status.

Many organizations start with Monitoring and discover that these tools lack in-depth information crucial for understanding why issues occur and how they impact the business. Therefore, organizations seek Observability to obtain the necessary data for in-depth analysis. This allows them to gain a deeper understanding of problems, enabling them to identify causes and assess the resulting impact on the business.

DevOps must strive to maintain high application readiness and the ability to scale from the existing structure automatically. The complexity of systems using microservices architecture makes it challenging to pinpoint the source or cause of issues when they arise. Traditional methods and tools fall short in managing the data volume generated by microservices systems. They may not provide sufficient information or understanding of the application's status for quick issue resolution.

However, fortunately, there are tools and methods to address these challenges.

Smart software like Dynatrace, capable of collecting and analyzing data that scales automatically to understand multicloud environments with its AI-powered tool Davis, can filter vast amounts of data from various sources quickly. The Davis AI helps filter high-speed data and analyze it through a unified interface.

Having a single source of truth reduces the problem of information silos, where teams cannot freely access and use data across functions. Centralizing eliminates this complex process and shifts from manual troubleshooting to automatic issue detection and smoother problem resolution.

In conclusion, I hope this helps readers better understand the differences between Monitoring and Observability.

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