In IT and cloud computing, observability delivers deep insights into a system’s internal states and performance through external outputs, gaining a holistic understanding. This holistic understanding is focused on current and trending operating conditions across the digital infrastructure from networking to computing and to cloud, security, applications, and end-user experience. The level of observability in a system correlates with how quickly the root cause of an issue can be identified and resolved, reducing the need for extensive testing and coding efforts.
The term “observability” has its roots in various engineering and control theories, which encompass understanding self-regulating systems. Over time, it has evolved into a critical practice for managing complex software systems. Observability is an essential practice for maintaining the performance, reliability, and scalability of modern software systems. As the threat landscape is quickly evolving and dynamic system architectures increase in complexity and scale, IT teams are facing pressure to respond to these challenging issues across multi-cloud environments. To overcome these complex challenges, enterprises must utilize deep observability to their advantage.
In the realm of highly distributed systems and hybrid cloud, observability empowers cross-functional teams to comprehensively understand and address specific inquiries. It unveils the underlying causes of malfunctions, offering actionable insights for performance enhancement. Through observability, teams receive timely alerts about potential issues, allowing proactive mitigation before users are affected.
Given the dynamic nature of modern cloud environments, numerous challenges emerge, often unforeseen and unmonitored. Observability tackles the dilemma of "unknown unknowns," continually providing visibility into emerging problems and their root cause.
As cloud-native architectures gain traction, organizations seek to embed Artificial Intelligence for IT Operations (AIOps), utilizing AI to automate processes across the DevSecOps life cycle. Integrating AI into tasks ranging from telemetry collection to full technology stack analysis furnishes reliable insights, facilitating automated application monitoring, testing, continuous delivery, security, and incident response.
Observability's value extends beyond IT applications. The collection and analysis of observability data provide precious insights into the business impact of digital services. This visibility enables optimization of conversions, validation of software alignment with business objectives, measurement of user experience SLO outcomes, and informed prioritization of business decisions. When an observability solution harnesses synthetic and real-user monitoring to analyze user experience data, organizations gain the ability to detect issues proactively and design enhanced user experiences grounded in real-time feedback.
Today’s threat landscape is quickly evolving with threat actors more sophisticated than ever. The digital transformation and the prevalence of complex, distributed, and interconnected systems have given rise to the need for real-time insights into the behavior and performance of these systems. Organizations will need to use observability for:
Although observability and monitoring are closely related and can complement one another, the two are not the same. With monitoring, teams will typically preconfigure dashboards to alert them once a performance issue occurs. To do so, this requires teams to already know what problems will occur before they've even encountered them. Monitoring is a very reactive method and isn’t suitable for cloud native environments where issues are complex and dynamic. This means teams will be unable to know in advance what type of issues might occur.
Observability on the other hand is proactive because the data offers insights within the whole environment internally and externally. Observability data is suited for exploring and finding the root cause quickly before it becomes an issue. This allows complete visibility of complex issues that may have not been anticipated beforehand.
Observability continuously gathers four performance telemetry data types by seamlessly integrating with the pre-existing instrumentation present in application and infrastructure components. Additionally, these platforms offer tools that enable the easy addition of instrumentation to these components, ensuring a comprehensive and ongoing collection of data. The four primary telemetry types are:
However, while metrics, events, logs, and traces (MELT) provide an application-focused or top-down view of system operations, they may not offer insights into network-related issues without understanding network activity. This is where solutions like Gigamon come into play, providing network-derived intelligence and complete performance management. Gigamon helps bridge the gap between application performance and network behavior, enabling a comprehensive understanding of system operations and facilitating effective issue resolution.
Observability is an essential component of any organization and encompasses many benefits to help understand complex issues with an observable system. Overall, it creates an environment that is easier to monitor, safer to update new code, and easier to respond to and repair. Observability directly supports Agile, DevOps, and SRE teams with delivering high quality and faster software.
To achieve observability, proper tools are crucial for collecting relevant telemetry data from your systems and applications. Creating an observable system involves developing your own tools, utilizing open-source software, or investing in a commercial observability solution. The implementation of observability generally entails four key components:
Instrumentation
These measurement tools gather telemetry data from various components, such as containers, services, applications, and hosts, ensuring comprehensive visibility across your entire infrastructure.
Data Correlation
Collected telemetry data undergoes processing and correlation, establishing context and enabling automated or customized data curation for generating time series visualizations.
Incident Response
Incident management and automation technologies ensure timely delivery of outage data to the appropriate individuals or teams based on on-call schedules and technical expertise.
AIOps
Utilizing machine learning models, AIOps aggregates, correlates, and prioritizes incident data, reducing alert noise, identifying potential system-impacting issues, and expediting incident response.
By strategically integrating these components, organizations enhance observability, leading to better insights, more efficient incident management, and improved system responsiveness.
In conjunction with MELT's top-down perspective, Gigamon technology ensures that every crucial detail, spanning physical or virtual networks, cloud services, or applications, is efficiently collected and seamlessly delivered to monitoring and analytics tools. By amplifying observability with intelligent traffic filtering, transformation, and forwarding capabilities, Gigamon empowers IT teams to not just monitor but to deeply understand and optimize their infrastructure. This profound understanding facilitates informed decision-making, swift troubleshooting, anomaly detection, and ultimately, the enhancement of operational efficiency and user experiences.
See the tech. Touch the tech.