Logs are crucial for monitoring system performance and ensuring effective observability, which is the comprehensive understanding of a system's health and functionality. The process of log analysis begins with log generation, collection, and storage, which are essential steps for deriving insights that feed into observability. Tools like Coralogix streamline this process by integrating with various platforms to automate log collection and storage, offering features like machine learning-powered cost optimization to manage storage expenses. Once collected, logs can be queried and aggregated to reveal trends and anomalies that signal potential issues or performance bottlenecks. Advanced querying capabilities, such as those offered by Coralogix, allow for cross-comparison of logs across different platforms, enhancing the ability to visualize and interpret data through tools like Kibana. Machine learning aids in identifying subtle anomalies that may indicate system problems, while features like Benchmark Reports and Version Tags help track changes and their impacts. Implementing these practices can be complex, but platforms like Coralogix offer scalable solutions, reducing the burden on development teams and improving system observability.