Loki
Develop the skills to implement a low-cardinality log query workflow through a learning path centred on Loki. Explore concepts, guided practice and an applied project.
Build practical Loki skills.
Skills and knowledge developed in this course.
- Loki data model
- Collection pipeline
- LogQL
- Log operations
- Applied project: Implement a low-cardinality log query workflow
- Loki — validation and review
About this course
This course develops practical capability in Loki. Its course-specific content covers Loki data model, Collection pipeline, LogQL, Log operations, followed by an applied project focused on implement a low-cardinality log query workflow. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with Loki or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of monitoring, observability & sre. Prior experience with the named product helps with the practical exercises.
Implement a low-cardinality log query workflow
Deliver implement a low-cardinality log query workflow. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Loki data model
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- Streams, labels and log content
- Index scope and label-cardinality decisions
Practical exerciseApply loki data model to the implement a low-cardinality log query workflow and capture the validation evidence.
02Collection pipeline
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- Agent configuration and parsing stages
- Timestamp handling and sensitive-data filtering
Practical exerciseApply collection pipeline to the implement a low-cardinality log query workflow and capture the validation evidence.
03LogQL
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- Log filters and parsing expressions
- Metric queries derived from logs
Practical exerciseApply logql to the implement a low-cardinality log query workflow and capture the validation evidence.
04Log operations
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- Retention, tenant boundaries and storage
- Diagnose missing logs and validate query performance
Practical exerciseApply log operations to the implement a low-cardinality log query workflow and capture the validation evidence.
05Applied project: Implement a low-cardinality log query workflow
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- Define inputs, assumptions and acceptance criteria for: Implement a low-cardinality log query workflow.
- Implement the project using loki data model, collection pipeline, logql, log operations.
Practical exerciseImplement a low-cardinality log query workflow.
06Loki — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Loki data model · Collection pipeline
Establish the Loki scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
LogQL · Log operations
Apply the core Loki techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for implement a low-cardinality log query workflow. - 04Learning milestone 4 In progress
Deliver the applied project
Complete implement a low-cardinality log query workflow, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| SRE / Observability Engineer | ₹8–14 LPA | ₹18–32 LPA | ₹35–58 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗