Description

This curriculum spans the technical and operational complexity of a multi-workshop infrastructure rollout, covering the design, deployment, and governance tasks typically managed by a dedicated observability or data platform team operating ELK at enterprise scale.

Module 1: Architecture and Sizing of ELK Infrastructure

Selecting appropriate node roles (ingest, master, data, coordinating) based on workload patterns and cluster scalability requirements.
Determining shard count and size per index to balance query performance and cluster management overhead.
Calculating memory allocation for heap and filesystem cache to prevent garbage collection spikes and optimize search latency.
Designing cross-cluster search topology for multi-region deployments with latency and failover constraints.
Choosing between hot-warm-cold architectures based on data access frequency and retention policies.
Planning node disk layout with separate mounts for data, logs, and temporary files to isolate I/O contention.
Implementing rolling upgrade procedures with version compatibility checks for plugins and ingest pipelines.
Configuring JVM settings aligned with garbage collector type and hardware specifications to avoid long pause times.

Module 2: Data Ingestion Pipeline Design

Deciding between Logstash, Beats, or direct API ingestion based on data volume, transformation needs, and latency SLAs.
Configuring Logstash pipeline workers and batch sizes to maximize throughput without exhausting CPU or memory.
Implementing dead-letter queues for failed document processing with reprocessing workflows and alerting.
Designing conditional filtering logic in Logstash to route or drop events based on business rules.
Securing Beats-to-Logstash/Elastic communication using TLS and mutual authentication.
Managing file harvesting state in Filebeat across restarts and log rotation scenarios.
Validating schema conformance at ingestion using ingest node pipelines with conditional failure handling.
Throttling high-volume data sources to prevent backpressure and cluster instability.

Module 3: Index Lifecycle and Retention Management

Defining index templates with appropriate mappings, settings, and versioning for evolving data schemas.
Implementing ILM policies to automate rollover based on index age, size, or document count.
Configuring snapshot lifecycles for compliance-driven retention with S3 or shared filesystem repositories.
Setting up data stream routing for time-series data with automated backing index management.
Enforcing retention boundaries using curator scripts or ILM delete phases with audit logging.
Migrating legacy indices to data streams without disrupting active ingestion or querying.
Monitoring index growth trends to forecast storage needs and adjust rollover thresholds.
Handling schema drift across index generations using dynamic templates and runtime fields.

Module 4: Search and Query Optimization

Designing field mappings to minimize indexing overhead (e.g., disabling norms for non-scoring fields).
Selecting appropriate analyzers for text fields based on language, search patterns, and performance impact.
Optimizing query DSL for filter context usage to leverage query cache and avoid scoring overhead.
Implementing result pagination using search_after instead of from/size for deep pagination.
Configuring index sorting to pre-sort documents on disk for frequent sort field queries.
Using field and index data tiers to exclude irrelevant data from search scope.
Diagnosing slow queries using profile API and adjusting query structure or hardware resources.
Managing wildcard and regex queries with circuit breakers and rate limiting to prevent cluster overload.

Module 5: Security and Access Control

Implementing role-based access control (RBAC) with granular index and document-level permissions.
Integrating Elasticsearch with LDAP or SAML for centralized user authentication and group mapping.
Configuring field-level security to mask sensitive data in search and aggregation results.
Enabling audit logging for security-sensitive operations (e.g., user changes, index deletions).
Rotating TLS certificates and API keys on a defined schedule with zero-downtime procedures.
Hardening cluster communication with encrypted internode traffic and firewall rules.
Managing service accounts for automation tools with minimal required privileges.
Validating input sanitization in Kibana dashboards to prevent script injection attacks.

Module 6: Monitoring and Alerting Strategy

Deploying Metricbeat to monitor Elasticsearch node health, JVM, and filesystem metrics.
Configuring alert thresholds for critical cluster states (e.g., red status, disk watermark breaches).
Building custom dashboards in Kibana to track ingestion rates, query latency, and error rates.
Setting up watch conditions in Watcher to trigger actions on log pattern anomalies.
Integrating alerts with external systems (e.g., PagerDuty, Slack) using webhooks with retry logic.
Managing alert fatigue by deduplicating and suppressing low-severity notifications.
Using the Elasticsearch Task Manager API to detect and resolve long-running operations.
Validating alert reliability through periodic test firings and response time measurement.

Module 7: Backup, Recovery, and Disaster Planning

Designing snapshot frequency and repository layout to meet RPO and RTO requirements.
Testing restore procedures for partial index recovery and full cluster rebuild scenarios.
Encrypting snapshot data at rest using repository-level encryption or cloud KMS.
Validating snapshot integrity using consistency checks and automated verification jobs.
Replicating snapshots across geographic regions for cross-site disaster recovery.
Managing snapshot retention with automated cleanup policies to control storage costs.
Documenting recovery runbooks with step-by-step instructions for different failure modes.
Simulating node and zone failures to validate cluster resilience and rebalancing behavior.

Module 8: Performance Tuning and Capacity Planning

Profiling indexing throughput under load to identify bottlenecks in disk I/O or CPU.
Adjusting refresh intervals based on data freshness requirements and indexing load.
Enabling compressed storage for _source and stored fields to reduce disk utilization.
Pre-sizing indices using benchmark data to project long-term storage and node count.
Monitoring merge policy behavior to prevent excessive segment count and search degradation.
Scaling horizontally by adding data nodes versus vertically by increasing node resources.
Using shard allocation filtering to isolate workloads and balance resource usage.
Conducting load tests with realistic query and ingestion patterns before production rollout.

Module 9: Governance and Compliance Integration

Implementing data masking or pseudonymization in ingest pipelines for PII fields.
Enforcing data retention policies aligned with GDPR, HIPAA, or SOX requirements.
Generating compliance reports from audit logs for access and configuration changes.
Classifying data at ingestion using tags or metadata for regulatory tracking.
Restricting export capabilities in Kibana to prevent unauthorized data exfiltration.
Integrating with enterprise data governance tools for metadata cataloging and lineage.
Validating encryption of data in transit and at rest during compliance audits.
Managing index ownership and stewardship through documented data custodian assignments.