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Highlights
- While systems generate vast metrics, logs, and traces, organisations struggle to quickly interpret this data and resolve issues before they impact performance.
- Organisations require an observability framework that delivers full visibility, enables root cause analysis, and drives continuous service improvement.
- Developing a full stack observability platform can ensure that logging, auditing, and error handling automatically produces meaningful data to monitor, troubleshoot, and improve the system.
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Why observability?
Organisations looking to monitor and understand the internal state of systems can analyse metrics, logs, and traces using observability platforms.
These platforms can detect, diagnose, and resolve issues faster, improving reliability and application performance.
Recent studies have indicated that observability platforms are used to address fundamental business requirements such as proactive resilience and up-time, cost optimisation, and enhancing user experience. When systematically applied, observability can also improve an organisation’s response time to problems and optimise business operations in real time.
In the retail industry, for example, an e-commerce platform can utilise observability to track user interactions, identify bottlenecks in the purchasing process, and analyse transaction failures in real time. These insights can play a key role in ensuring the platform runs smoothly during peak shopping periods, resulting in better customer experience, optimal operational efficiency, and increased sales.
Legacy monitoring
Inefficient tools and traditional monitoring can limit observability platforms.
While there are many open-source and commercial off-the-shelf (COTS) products available, they often provide piecemeal solutions. This can affect application performance and compatibility, resulting in security and compliance concerns.
Additionally, licence costs and the hesitancy to deploy agents on applications to emit telemetry data pose further challenges. For instance, an enterprise service bus (ESB) tool might make agent deployment difficult due to compatibility issues and complex telemetry configuration.
Organisations need a strong observability framework that provides end-to-end visibility, identifies root causes, and continuously improves services.
Full stack approach
Developing a full stack observability platform can ensure that logging, auditing, and error-handling automatically produce meaningful data.
This data can then be used to monitor, troubleshoot, and improve the system. To achieve a full stack observability platform, proper planning during the design phase is imperative.
The following diagram (see Figure 1) illustrates how an enterprise-grade observability platform transforms raw telemetry into actionable insight, enabling faster decisions and resilient operations.
Figure 1: Telemetry flow or pipeline
Publish telemetry data to a centralised telemetry collector: Ensure applications or services send telemetry data to a centralised telemetry collector. This can be done via message queues or application programming interfaces (APIs), allowing for asynchronous data transmission. For instance, in a microservices architecture for an e-commerce platform, the inventory management process should publish its performance metrics like metadata, response time, and error details to an Apache ActiveMQ message queue. This will enable real-time monitoring of a service without impacting performance.
Monitor telemetry data and publish to the storage: Instruct the telemetry collector to monitor incoming telemetry data and publish to a persistent storage solution, such as a database. This ensures data is retained for further analysis and reporting. For example, a logistics company collects telemetry data from various shipping services. Upon receiving this data from the message queue, the telemetry collector writes it to a PostgreSQL database, which stores information like delivery times and status updates for historical tracking and reporting.
Process telemetry data from storage: Extract meaningful insights from the stored telemetry data using the metrics, events, logs, and traces (MELT) principle. This helps create a comprehensive view of system performance and user experience. An order service where Python scripts are used to process telemetry data from their PostgreSQL database, for example, provides meaningful new data. It analyses telemetry data, identifies trends in transaction behaviour, and correlates them with its chain transactions to ensure seamless user experience.
Create an observability dashboard: Visualise the processed telemetry data with a dashboard, which will provide insights into system performance, errors, and user interactions. This will facilitate quicker decision-making and proactive problem resolution. For instance, build a dashboard using Django framework to visualise key metrics such as end-to-end traceability, elapsed time at each hop during its transaction journey, success or failure reason, self-healing, auto-ticketing, and generate analytics and insights from telemetry data. This will help the operations team to quickly identify issues and improve service reliability, ultimately enhancing customer satisfaction.
Benefits
Implementing an observability framework allows businesses to efficiently monitor and analyse system performance.
This will ensure faster issue resolution and maintenance of high-quality user experience.
Integrating telemetry data collection, processing, and visualisation gives organisations valuable insights into their operations.
Systems that avoid emitting or capturing payloads during tracing better meet data storage and efficiency requirements. Trace data helps visualise the time each service takes in a transaction. This allows for easy identification of broken chains or delays.
Better decisions
Observability platforms improve decision-making and provide companies with a competitive advantage.
Collecting and correlating telemetry signals from the outside empowers businesses to proactively detect unusual behaviour, diagnose issues faster, and prevent disruptions before they impact business.
Built on open-source, licence-free technology, and designed to scale, this approach eliminates the need for agents while supporting all telemetry signals–metrics, events, logs, and traces–within a single framework. The result is improved business insights through end-to-end visibility, reduced alert fatigue, faster root-cause analysis, and efficient incident management.
Ultimately, observability becomes more than a monitoring capability–it lays the foundation for continuous improvement, automation, and innovation, driving better operational resilience and stronger business outcomes.