Modern DevOps teams process over 2TB of log data daily, yet 67% struggle with efficient log analysis during critical incidents. This Short Course was created to help IT Support and Operations professionals accomplish building robust log observability systems that enable rapid troubleshooting and proactive monitoring. By completing this course, you'll master LogQL query optimization, label cardinality management, and integrated logging workflows that reduce mean time to resolution from hours to minutes. By the end of this course, you will be able to: Apply LogQL filters, parsers, and aggregation operators to isolate error patterns and generate on-call alerts from Loki log streams, Analyze label cardinality and retention configurations to optimize Loki query performance and storage cost for a multi-cluster environment, and Evaluate a logs-to-traces troubleshooting workflow to confirm root cause remediation and document incident lessons learned. This course is unique because it combines hands-on Loki deployment with real-world incident response scenarios, teaching both technical implementation and operational best practices for production environments. To be successful in this project, you should have a background in Linux system administration, container orchestration, and basic observability concepts.

Getting Started with Loki Observability

Getting Started with Loki Observability

Instructor: Hurix Digital
Access provided by Kaveri College of Arts, Science and Commerce
Recommended experience
What you'll learn
In observability systems, label design and cardinality control are critical for performance and cost in time-series databases.
Filtering logs before parsing reduces data volume early, improving efficiency and lowering computational overhead.
Correlating logs, metrics, and traces shows observability works best through unified, not siloed, monitoring approaches.
Structured logging reshapes incident response, requiring standardized workflows for modern distributed systems.
Details to know

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April 2026
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There are 3 modules in this course
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