Most logging practices lead to noise rather than clarity. Developers often over-log, capturing every function call and variable change, which creates vast amounts of data that are rarely useful. This clutter not only consumes storage and processing power but also complicates root cause analysis. Instead of providing insights, excessive logs make it harder to identify real issues. A more selective approach is necessary. Focus on logging information that aids in understanding system behaviors, debugging problems, or assessing business impacts. Effective logging should include context, enabling the reconstruction of events. Simple messages like "Error occurred" lack value without additional details such as request IDs or input parameters. Tools like OpenTelemetry facilitate this by linking logs to traces, enhancing their relevance. Structured logging formats such as JSON or CSV are more efficient than free text, allowing for easier querying and faster analysis. Patterns become visible when logs are well-structured, helping teams identify trends and correlations. Not every event warrants a log entry. For instance, if you're tracking user flows or latency, spans and metrics are more appropriate. Spans capture the context and relationships between services, revealing the reasons behind performance issues without the clutter of logs. Log levels should communicate intent clearly. Use ERROR, WARN, and INFO judiciously to convey the importance of each message. Tools like OpenTelemetry offer filtering options that help eliminate unnecessary logs during collection, ensuring that only meaningful data is stored and analyzed.