Fog Computing provides computational resources close to the end user, supporting low-latency and high-bandwidth communications. It supports IoT applications, enabling real-time data processing, analytics, and decision-making at the edge of the network. However, the high distribution of its constituent nodes and resource-restricted devices interconnected by heterogeneous and unreliable networks makes it challenging to execute service maintenance and troubleshooting, increasing the time to restore the application after failures and not guaranteeing the service level agreements. In such a scenario, increasing the observability of Fog applications and services may speed up troubleshooting and increase their availability. An observability system is a data-intensive service, and Fog Computing could have its nodes and channels saturated with an additional load. In this work, we detail the three pillars of observability (metrics, log, and traces), discuss the challenges, and clarify the approaches for increasing the observability of services in Fog environments. Furthermore, the system architecture that supports observability in Fog, related tools, and technologies are presented, providing a comprehensive discussion on this subject. An example of a solution shows how a real-world application can benefit from increased observability in this environment. Finally, there is a discussion about the future directions of Fog observability.