Grafana Loki

Queries logs in Grafana Loki using basic LogQL syntax. Navigates Grafana dashboards to view application log streams. Understands label-based log filtering and basic log aggregation concepts.

Uses Grafana Loki to search and filter logs during incident investigation. Understands log retention policies and storage concepts. Follows runbooks that reference Loki queries for common troubleshooting scenarios.

Configures Loki ingestion pipelines with Promtail and structured metadata extraction. Builds Grafana dashboards combining Loki logs with Prometheus metrics for correlated observability. Sets up log-based alerting rules for platform health monitoring.

Configures Loki for multi-tenant log aggregation across services. Creates advanced LogQL queries with metric extraction for SLI tracking. Builds alerting rules on log patterns and participates in on-call rotation using log-based diagnostics.

Architects Loki deployment topology for high-throughput multi-cluster log aggregation. Designs log pipeline standards including labeling conventions, retention policies, and cost optimization. Integrates Loki into the platform observability stack alongside tracing and metrics.

Designs the organization-wide logging strategy with Loki as the centralized log platform. Defines SLI/SLO based on log-derived metrics and automates error-budget alerting. Leads post-mortems leveraging Loki correlation with distributed traces and APM data.

Adopts Grafana Loki as cost-effective logging solution for the platform: multi-tenant configuration, retention policies. Designs label strategy for optimal query performance. Integrates with Grafana for unified observability (logs + metrics + traces in single UI).

Defines Loki standards: label strategy (low cardinality), retention policies, query patterns. Implements Loki for cost-effective log aggregation. Compares Loki vs ELK by scenarios.

Defines logging strategy: Loki vs ELK vs managed solutions for various platform use cases. Designs Loki at scale: microservices mode, S3 backend, caching. Shapes vision for cost-efficient observability data platform with tiered storage.

Designs log aggregation strategy: Loki for Kubernetes-native logging, multi-tenant setup, long-term storage. Defines when Loki vs ELK vs managed (Datadog/Splunk).