Big data stacks and IT platforms run thousands of workloads and applications. To succeed, developers must pursue a shared understanding of how their apps function. To gain this understanding, observability is crucial. If you can’t truly observe your tech stacks, then there’s no way you can understand them. Observability is now a prerequisite for successful big data analytics operations.
IT service providers and business organizations need to keep their entire IT infrastructure performing at an optimal level to meet business requirements and consumer demands. This includes platform services, containers, and microservices. To ensure dependability and predictability, enterprises have to observe and monitor hardware, applications, and other metrics associated with infrastructure performance.
Many materials and articles discuss a supposed conflict: observability vs. monitoring. But the reality is that, when it comes to optimizing big data stacks, monitoring and observability are two different but closely related things. Monitoring pertains to creating and deploying systems to gather data, with the goal of planning and executing a data-driven response when something goes wrong. Observability is the practice of equipping those monitoring systems with the features and tools to collect actionable data that alerts users when an error has occurred and guides the user toward a speedy resolution.
Observability is now a crucial component in big data analytics optimization and performance tuning. However, there are degrees to it.