Description

This curriculum spans the equivalent of a multi-workshop technical engagement with an operations team, covering the design, implementation, and governance of data pipelines in ELK Stack at the level of detail required for production deployment across distributed systems.

Module 1: Architecture Planning for Scalable Data Ingestion

Selecting between Logstash, Beats, and direct HTTP input based on data source volume, latency requirements, and protocol support.
Designing index lifecycle policies during initial architecture to prevent unbounded index growth on ingestion spikes.
Allocating primary and replica shards based on node count, expected query load, and high availability requirements.
Implementing dedicated ingest nodes to isolate parsing load from search and storage functions.
Configuring persistent queues in Logstash to prevent data loss during downstream Elasticsearch outages.
Choosing between single-index and time-based index patterns based on retention policies and query performance needs.
Evaluating the use of Kafka as a buffer between data sources and Logstash for backpressure management.
Defining field naming conventions and data types early to ensure consistency across indices and avoid mapping conflicts.

Module 2: Ingest Node Pipeline Design and Optimization

Writing conditional pipelines in Ingest Node to route documents based on source, content, or metadata.
Using the grok processor to parse unstructured logs while managing performance impact from complex regex patterns.
Implementing date processor configurations to standardize timestamp formats across heterogeneous sources.
Adding metadata fields (e.g., environment, region) during ingestion to support downstream filtering and access control.
Optimizing pipeline execution order to minimize redundant processing and reduce CPU usage.
Handling partial failures in bulk requests by configuring on_failure blocks in pipeline definitions.
Using the script processor with Painless to enrich or transform fields not supported by built-in processors.
Monitoring pipeline processor execution times to identify bottlenecks in transformation logic.

Module 3: Index Management and Lifecycle Automation

Configuring Index Lifecycle Management (ILM) policies to automate rollover based on size or age thresholds.
Setting up data tiers (hot, warm, cold, frozen) and routing indices accordingly to optimize storage cost and performance.
Defining custom rollover aliases and coordinating with application teams to update ingestion endpoints.
Adjusting refresh intervals during peak indexing to balance search freshness with cluster resource consumption.
Implementing shrink and force merge operations during index transition from hot to warm phase.
Archiving indices to shared filesystem or S3-compatible storage using snapshot lifecycle policies.
Managing index templates to enforce consistent settings, mappings, and ILM policies across new indices.
Handling legacy index migration by reindexing into new ILM-managed indices with updated mappings.

Module 4: Query Performance Tuning and Search Optimization

Choosing between term and match queries based on field type, analysis requirements, and use case precision.
Using _source filtering to reduce response size when only specific fields are needed in results.
Implementing search templates to standardize complex queries used by multiple applications.
Configuring index sorting to pre-order documents and accelerate range-based queries.
Optimizing query DSL by avoiding wildcard leading terms and excessive use of script_score.
Monitoring slow query logs to identify and refactor inefficient aggregations or deep pagination.
Using point-in-time (PIT) searches for consistent results during large result set traversal.
Precomputing aggregations using data streams and rollup indices for historical reporting workloads.

Module 5: Security Configuration and Access Governance

Mapping LDAP/AD groups to Elasticsearch roles to enforce least-privilege access based on team function.
Creating field- and document-level security rules to restrict sensitive data exposure in search results.
Configuring API key management for service accounts used by automated ingestion pipelines.
Enabling TLS between nodes and clients, including certificate rotation procedures.
Auditing user activity by enabling audit logging and routing logs to a protected index.
Isolating indices by tenant in multi-customer environments using index patterns and role templates.
Managing snapshot repository access controls to prevent unauthorized data restoration or deletion.
Implementing dynamic Kibana spaces with role-based navigation to limit dashboard visibility.

Module 6: Monitoring and Alerting for Data Pipeline Health

Deploying Metricbeat on Elasticsearch nodes to collect JVM, thread pool, and filesystem metrics.
Creating alerting rules for critical conditions such as disk watermark breaches or node disconnects.
Tracking ingestion lag by comparing @timestamp with receive timestamp in Logstash or Beats.
Using Watcher to trigger alerts when specific error patterns appear across logs at high frequency.
Setting up dead-letter queue monitoring for failed documents in Logstash pipelines.
Correlating pipeline error rates with deployment events to identify configuration regressions.
Monitoring index queue sizes in Logstash to detect downstream indexing bottlenecks.
Validating data completeness by comparing source system record counts with indexed document counts.

Module 7: Kibana Data View and Dashboard Engineering

Designing data views with appropriate time fields and field formatting for cross-team usability.
Building reusable dashboard templates with input controls for dynamic filtering by team or service.
Configuring scripted fields in Kibana only when equivalent transformations cannot be done at ingest.
Optimizing dashboard load times by limiting the number of concurrent visualizations and time ranges.
Using saved searches as data sources for multiple visualizations to ensure query consistency.
Setting default index patterns in Kibana to align with current active data streams.
Managing dashboard versioning through exported JSON files in source control.
Implementing dashboard-level permissions via space membership and role assignments.

Module 8: Cross-Cluster Search and Data Federation

Configuring cross-cluster search to enable unified queries across production, staging, and DR clusters.
Managing remote cluster connection health and latency to avoid search timeouts.
Using federated roles to control which indices users can access across clusters.
Designing query routing strategies to minimize data transfer across high-latency network links.
Replicating critical indices using CCR for local query performance in geographically distributed teams.
Handling version skew between clusters by testing query compatibility before enabling federation.
Monitoring remote cluster request queues to detect ingestion or network bottlenecks.
Securing inter-cluster communication with dedicated API keys and TLS mutual authentication.

Module 9: Compliance, Retention, and Data Governance

Implementing automated index deletion policies based on data classification and regulatory requirements.
Generating audit reports for data access and modification using Elasticsearch query logs.
Masking sensitive fields (e.g., PII) in query results using ingest-time hashing or redaction.
Validating data retention compliance by scanning index metadata and ILM policy assignments.
Archiving specific indices to immutable storage for legal hold scenarios.
Documenting data lineage from source system to index for regulatory audits.
Coordinating with legal teams to define data minimization rules for ingestion pipelines.
Conducting periodic access reviews to deactivate unused roles and indices.