Description

This curriculum spans the equivalent of a multi-workshop operational immersion, covering the same technical breadth and decision frameworks used in enterprise-scale monitoring deployments, from initial infrastructure planning to ongoing performance tuning and governance.

Module 1: Architecting Scalable ELK Infrastructure for Application Monitoring

Selecting between hot-warm-cold architectures and tiered node roles based on ingestion rate and retention requirements.
Designing index lifecycle management (ILM) policies to automate rollover, shrink, and deletion of time-series application logs.
Calculating shard sizing and distribution to balance query performance with cluster overhead in high-volume environments.
Implementing dedicated ingest nodes to offload parsing from data nodes under sustained log throughput.
Configuring persistent queues in Logstash to prevent data loss during downstream Elasticsearch outages.
Planning network segmentation and firewall rules to secure internal communication between Beats, Logstash, and Elasticsearch.

Module 2: Instrumenting Applications for Effective Log Collection

Standardizing log formats across polyglot microservices using structured logging libraries (e.g., log4j2 JSON layout, Bunyan, Serilog).
Configuring Filebeat modules or custom prospector inputs to tail application log files with correct encoding and multiline support.
Setting log level thresholds in production to minimize noise while preserving debug data for critical components.
Embedding correlation IDs in log entries to enable end-to-end tracing across service boundaries.
Managing log rotation policies on hosts to prevent disk exhaustion while ensuring Filebeat can recover file offsets.
Validating timestamp accuracy and time zone consistency across distributed application servers to maintain event ordering.

Module 3: Enriching and Transforming Log Data in the Pipeline

Using Logstash mutate and date filters to normalize field types and timestamps before indexing.
Joining log events with static metadata (e.g., host roles, environment tags) via lookup tables or Elasticsearch dictionaries.
Applying conditional parsing rules in pipelines to handle variable log formats from legacy and modern applications.
Redacting sensitive data (e.g., PII, tokens) using grok patterns and mutate filters in compliance with data governance policies.
Deploying pipeline workers and batch sizes tuned to CPU and memory constraints on ingestion nodes.
Versioning and testing Logstash configurations in staging to prevent parsing failures in production pipelines.

Module 4: Designing Searchable Schemas and Index Templates

Defining dynamic index templates with appropriate mappings to prevent mapping explosions from unstructured fields.
Setting explicit field data types (keyword vs. text, scaled_float for metrics) to optimize storage and query speed.
Configuring custom analyzers for application-specific fields like request URIs or error messages.
Disabling _source for high-volume indices when retrieval is unnecessary, balancing storage savings against debug limitations.
Implementing runtime fields to compute derived values (e.g., SLA status) without reindexing historical data.
Managing alias strategies to support zero-downtime index rollovers and seamless log stream continuity.

Module 5: Building Actionable Dashboards and Visualizations

Constructing time-series visualizations for error rates, latency percentiles, and throughput using Lens or TSVB.
Designing dashboard drilldowns that link high-level KPIs to raw log events for root cause analysis.
Aggregating logs by service, host, and deployment version to isolate performance regressions.
Using tags and color coding in dashboards to reflect environment (prod/staging) and severity levels.
Setting appropriate time ranges and refresh intervals to prevent performance degradation in shared dashboards.
Validating dashboard usability with incident response teams to ensure relevance during outages.

Module 6: Implementing Alerting and Anomaly Detection

Configuring threshold-based alerts on log-derived metrics (e.g., 5xx error rate > 5% over 5 minutes).
Using machine learning jobs in Elasticsearch to detect anomalies in log volume or error patterns without predefined thresholds.
Defining alert deduplication and throttling policies to avoid notification fatigue during sustained outages.
Routing alerts to appropriate channels (e.g., Slack, PagerDuty) based on service criticality and on-call schedules.
Testing alert conditions with historical log data to verify sensitivity and reduce false positives.
Logging alert trigger and resolution events to a separate index for audit and post-mortem analysis.

Module 7: Securing and Governing the Monitoring Environment

Implementing role-based access control (RBAC) to restrict index and dashboard access by team and environment.
Enabling TLS encryption between Beats, Logstash, and Elasticsearch nodes across all transport layers.
Auditing user actions in Kibana using audit logging to meet compliance requirements (e.g., SOC 2).
Masking sensitive fields in search results using field-level security policies.
Regularly rotating service account credentials used by Filebeat and Logstash to access Elasticsearch.
Establishing data retention SLAs and automating deletion via ILM to comply with data sovereignty regulations.

Module 8: Optimizing Performance and Total Cost of Ownership

Profiling slow queries using the Elasticsearch profile API and optimizing with targeted indexing strategies.
Compressing older indices using best_compression settings and transitioning to cold nodes with lower IOPS.
Right-sizing JVM heap for data nodes to avoid garbage collection pauses without underutilizing memory.
Monitoring cluster health metrics (e.g., queue sizes, thread pools) to preempt ingestion bottlenecks.
Conducting load testing with realistic log volumes to validate cluster capacity before major releases.
Consolidating redundant dashboards and disabling unused visualizations to reduce Kibana backend load.