Logging is a crucial component for monitoring the health and performance of applications, especially as they scale and require centralized log management across various systems. Python, a versatile and popular programming language, offers a flexible logging module that supports multiple log levels and formats, including JSON for structured logging. Leveraging the ELK stack, comprising Elasticsearch, Logstash, and Kibana, enhances log management by enabling efficient data processing, search, and visualization. Developers can use tools like Filebeat to ship logs to Elasticsearch, or opt for a managed service like Logz.io to simplify ELK stack management. JSON-formatted logs are particularly advantageous for integration with these tools, though non-JSON logs can also be accommodated with additional parsing configurations. The article highlights the ease of setting up Python logging and the benefits of using centralized systems like ELK or Logz.io for advanced log analysis and application monitoring, while also noting the potential pitfalls and solutions for legacy systems or unexpected application crashes.