Description

This curriculum spans the technical and operational rigor of a multi-workshop infrastructure rollout, covering the same depth of configuration, integration, and governance tasks typically addressed in enterprise-grade logging platform deployments.

Module 1: Architecture Design and Sizing for Production ELK Deployments

Selecting appropriate node roles (ingest, master, data, coordinating) based on workload patterns and availability requirements.
Determining shard count and size per index to balance query performance, recovery time, and cluster overhead.
Designing multi-zone or multi-region cluster topologies to meet RPO and RTO objectives for critical logging systems.
Calculating storage capacity with retention policies, compression ratios, and growth projections over 12–18 months.
Integrating ELK with existing DNS, load balancing, and firewall policies in enterprise network zones.
Evaluating hardware vs. cloud-managed (AWS OpenSearch, Elastic Cloud) based on compliance, cost, and operational control needs.

Module 2: Log Ingestion Pipeline Configuration and Optimization

Configuring Filebeat modules or custom prospector settings to handle log rotation, multiline events, and file truncation.
Implementing Logstash pipelines with conditional filters to parse heterogeneous log formats while minimizing CPU overhead.
Tuning pipeline workers, batch sizes, and queue types (in-memory vs. persistent) to prevent backpressure during traffic spikes.
Securing Beats-to-Logstash or Beats-to-Elasticsearch communication using TLS and certificate pinning.
Validating schema consistency across sources using ingest node pipelines with conditional failure handling.
Managing pipeline versioning and deployment using CI/CD workflows with rollback capabilities.

Module 3: Index Management and Data Lifecycle Policies

Creating index templates with appropriate mappings to avoid dynamic mapping explosions and field type conflicts.
Implementing ILM (Index Lifecycle Management) policies for rollover, shrink, force merge, and deletion phases.
Setting up data streams for time-series logs and aligning them with application deployment cycles.
Managing cold/frozen tiers using shared filesystems or S3-backed repositories with snapshot lifecycle policies.
Handling index bloat from high-cardinality fields by enforcing field limits and using keyword demotion strategies.
Coordinating reindexing operations during schema migrations with zero-downtime constraints.

Module 4: Security Configuration and Access Control

Configuring role-based access control (RBAC) with granular index and document-level permissions for teams.
Integrating Elasticsearch with LDAP or SAML providers while mapping external groups to internal roles.
Enabling field and document-level security to restrict access to PII or sensitive system logs.
Managing API key lifecycles and service accounts for automated tools and monitoring integrations.
Implementing audit logging for cluster configuration changes and user search queries.
Hardening cluster communication with TLS certificates, cipher suite restrictions, and hostname verification.

Module 5: Monitoring, Alerting, and Cluster Health Management

Deploying Elastic Agent or custom exporters to monitor JVM, thread pools, and filesystem usage across nodes.
Setting up alerts for critical conditions such as disk watermark breaches, unassigned shards, or node failures.
Using Kibana Observability to correlate search latency with indexing load and garbage collection events.
Establishing baseline performance metrics for normal operation to detect anomalies in query or ingestion patterns.
Configuring alert suppression windows and notification routing based on on-call schedules and severity tiers.
Validating alert fidelity by tuning thresholds to minimize false positives from transient spikes.

Module 6: Backup, Recovery, and Disaster Preparedness

Registering and managing snapshot repositories with access controls and encryption at rest.
Scheduling regular snapshots aligned with RPO requirements and verifying snapshot integrity.
Testing full cluster recovery in isolated environments to validate RTO and dependency resolution.
Handling partial restores of indices or aliases during incident response without disrupting live operations.
Documenting recovery runbooks with step-by-step procedures for node, index, and cluster-level failures.
Coordinating cross-cluster replication for business-critical indices with lag monitoring and conflict resolution.

Module 7: Performance Tuning and Query Optimization

Identifying slow queries using the search slow log and optimizing with appropriate filters or aggregations.
Designing custom analyzers and disabling unnecessary full-text fields to reduce indexing overhead.
Using doc_values and keyword fields for aggregations instead of text fields to improve performance.
Adjusting refresh intervals for high-throughput indices during batch ingestion windows.
Scaling coordinating nodes independently to absorb client request bursts without affecting data nodes.
Profiling query execution with the Profile API to diagnose costly boolean queries or nested operations.

Module 8: Integration with Enterprise Tooling and Change Governance

Embedding Kibana dashboards into SIEM or ITSM platforms using iframe restrictions and token-based access.
Automating index template deployment via Terraform or Ansible with change tracking in version control.
Enforcing peer review and approval workflows for changes to ingest pipelines or cluster settings.
Integrating with centralized logging standards (e.g., RFC5424, CEF) for cross-platform correlation.
Managing Kibana space permissions and saved object ownership to prevent configuration drift.
Aligning ELK change windows with enterprise CAB processes and change freeze periods.