Observability has been gaining importance in big data IT because it not only tells you when downtime or an error happened but why it happened.
With the rise of new technologies like cloud, DevOps, microservices, containers, and virtualization, IT & DevOps teams are working on simultaneously boosting speed and reducing friction throughout the code to generation lifecycle.
IT & DevOps teams are firefighting complex new challenges every day. Facing such challenges, observability is crucial to completely grasp what is actually occurring in your business’s applications and systems. With observability, enterprises can fully optimize the performance of their big data stacks and IT environments and effectively maximize value in a way that isn’t possible with monitoring alone.
What is Observability?
Observability enables DevOps and IT teams to gather real-time information from various sources, including performance monitoring solutions and logs, to create a complete picture of their big data stacks and IT environments.
An accurate observability definition might say: Observability is the ability of IT and DevOps teams to fully understand what is going on within multi-layered architectures and take complete advantage of real-time performance data to optimize performance.
Observability makes it viable for developers and IT administrators to organize data from fragmented and disparate sources to derive reliable and actionable knowledge of the environment. DevOps and IT admins are empowered with an understanding of how deep and impactful performance issues are, enabling them to troubleshoot at a root level.
When big data stacks and IT infrastructure are observable, navigation from effect to cause becomes faster and simpler. Root cause analysis can be complex and requires several steps, including any number of innocent intermediaries. Observability allows admins and developers to complete those steps much more quickly.
Observability vs. Monitoring
The reality is there is no observability vs. monitoring battle going on. Both are related terms within the big data and cloud perspectives. In the stages of observability, monitoring comes first. Later, true observability arrives.
Developers and IT admins require performance data and other essential information to evaluate and further improve tasks, track performance, identify anomalies, and optimize processes. Monitoring refers to the collection and visualization of this data. It lets users know whether the system is working or not, and alerts them when anomalies or issues are detected. This allows for a rapid and viable response.
Observability builds on this. It combines massive amounts of data from monitoring tools and logs and infers using artificial intelligence and machine learning to generate actionable insights. Users are not only alerted when an error or issue arises but—more importantly— why it occurs. With observability, DevOps and IT administrators deploy rapid responses to incidents, significantly reducing downtime and minimizing its impact on end-users.
Insights derived from observability create a holistic picture of the environment, helping IT teams and DevOps determine exactly what to monitor more closely, and how to optimize performance. Only with observability can enterprises transition from reactive to proactive to predictive IT. But observability vs. monitoring? That’s a bit of a mirage. The former follows the latter.
The Benefits of Observability
Actionable Insights via End-to-End Visibility. True observability gives users the “why” of big data incidents, problems, and anomalies. Armed with a comprehensive picture of the environment, plus access to insights, DevOps and IT teams can understand why these events happen and