Description

This curriculum spans the equivalent of a multi-workshop operational immersion, addressing the full lifecycle of log storage in the ELK Stack as it would be implemented, maintained, and optimized across distributed systems, security frameworks, and compliance regimes in medium to large enterprises.

Module 1: Architecture Design and Sizing for ELK Deployments

Selecting between hot-warm-cold architectures based on query latency requirements and retention policies for time-series log data.
Calculating shard count and size per index to balance query performance and cluster overhead in production Elasticsearch environments.
Dimensioning heap size for Elasticsearch nodes to avoid garbage collection pauses while maintaining efficient memory utilization.
Designing index lifecycle policies that align with compliance requirements and storage cost constraints.
Choosing replication factor based on availability needs and total storage overhead in multi-datacenter deployments.
Planning network topology to isolate ingest, data, and coordinating nodes for performance and security segmentation.

Module 2: Log Ingestion Pipeline Configuration

Configuring Filebeat modules to parse structured logs from common services (e.g., Nginx, MySQL) without custom scripting.
Setting up Logstash pipelines with conditional filters to route logs based on source, severity, or content patterns.
Tuning Logstash input and output queue sizes to prevent backpressure during traffic spikes.
Implementing dead-letter queues in Kafka to capture failed log events for reprocessing and debugging.
Validating JSON log schema consistency at ingestion using Logstash dissect or grok patterns with fallback handling.
Securing Beats-to-Logstash communication using TLS with mutual authentication in regulated environments.

Module 3: Index Management and Data Lifecycle Policies

Defining ILM (Index Lifecycle Management) policies to automate rollover based on index size or age in daily indices.
Configuring shrink and force merge operations during the transition from hot to warm phases to reduce shard overhead.
Setting up data streams for time-series logs to simplify write aliases and lifecycle automation.
Managing index templates with versioned priority to support schema evolution across multiple application versions.
Archiving stale indices to object storage using snapshot repositories with retention scheduling and access auditing.
Enforcing field limits and mapping depth to prevent mapping explosions from unstructured application logs.

Module 4: Search Performance and Query Optimization

Designing custom analyzers for application-specific log fields such as trace IDs or error codes to improve search precision.
Using runtime fields to extract and query transient data without modifying the original index mapping.
Optimizing Kibana dashboard queries by replacing wildcard searches with term-level filters and date range constraints.
Implementing search templates with parameterized queries to standardize access patterns and prevent expensive queries.
Monitoring slow query logs in Elasticsearch to identify and refactor inefficient aggregations or scripted fields.
Configuring point-in-time (PIT) searches for consistent results during long-running forensic investigations.

Module 5: Security and Access Control Implementation

Defining role-based access control (RBAC) in Kibana to restrict index pattern visibility by team or environment.
Integrating Elasticsearch with LDAP or SAML for centralized user authentication and group synchronization.
Encrypting at-rest data on Elasticsearch data nodes using internal encryption or filesystem-level tools.
Applying index-level security to isolate production logs from staging environments within a shared cluster.
Configuring audit logging in Elasticsearch to track administrative actions and unauthorized access attempts.
Masking sensitive fields (e.g., PII) in Kibana using field-level security without altering stored data.

Module 6: High Availability and Disaster Recovery Planning

Distributing Elasticsearch shards across availability zones to maintain availability during node or zone failures.
Testing cluster recovery procedures by simulating master node isolation and quorum loss scenarios.
Scheduling cross-cluster replication for critical indices to a secondary region for disaster recovery.
Validating snapshot integrity by restoring to a test cluster on a quarterly basis.
Implementing automated monitoring for unassigned shards and degraded replicas using cluster health APIs.
Documenting and version-controlling cluster configuration (e.g., templates, ILM policies) in source control for reproducibility.

Module 7: Monitoring, Alerting, and Operational Maintenance

Deploying Elastic Agent to monitor Elasticsearch node health, JVM usage, and disk pressure in real time.
Creating alerting rules for index write failures, high ingest latency, or sudden drops in log volume.
Scheduling regular snapshot backups with retention policies aligned to compliance SLAs.
Rotating TLS certificates for internal node communication before expiration without cluster downtime.
Upgrading Elasticsearch versions using rolling upgrades with plugin compatibility checks and pre-upgrade validations.
Managing disk watermarks and automated shard allocation to prevent out-of-disk scenarios in long-running clusters.

Module 8: Cost Optimization and Scalability Strategies

Transitioning older indices to compressed storage formats using index down-sampling or rollups for long-term retention.
Evaluating total cost of ownership (TCO) between self-hosted clusters and Elastic Cloud based on data volume and SLA.
Implementing log sampling for high-volume sources (e.g., access logs) when full ingestion exceeds budget.
Right-sizing data node instance types based on I/O patterns, memory pressure, and CPU utilization trends.
Using index templates to disable _source or enable source filtering for logs where field extraction is predictable.
Consolidating low-volume indices using index patterns and aliases to reduce management overhead.