A small infrastructure can generate terabytes of log data, making manual analysis impossible without a structured approach.
Enhance log messages by adding context like IDs, usernames, and system sources to make them truly useful for debugging.
By correlating common identifiers like timestamps and usernames across different systems, you can reconstruct a complete user journey.
Automate the process of finding patterns in logs by using techniques like pre-processing, common identifiers, and clustering algorithms.
The K-Means algorithm can automatically group millions of log lines by similarity, helping to identify issues like high CPU usage without manual sorting.
Use rule-based analysis for known, deterministic tasks, while K-Means excels at finding hidden patterns in complex data for true observability.
Go beyond error tracking by analyzing logs to measure key business performance indicators, such as user engagement or order processing time.
Improve system observability by designing meaningful logs with clear levels and messages before writing the application logic itself.
Jobs that call for the skills explored in this talk.