Description

This curriculum spans the equivalent depth and breadth of a multi-workshop operational immersion, covering the full lifecycle of indexing in ELK—from ingestion through sharding, security, and recovery—with the same technical specificity found in internal platform engineering playbooks.

Module 1: Understanding Data Ingestion Patterns in ELK

Select and configure Logstash input plugins based on source system protocols (e.g., syslog, Beats, JDBC) while managing connection timeouts and backpressure.
Design filebeat prospector configurations to monitor log rotation patterns without duplicating or missing events.
Implement JSON parsing in Logstash filters only when schema stability is confirmed; otherwise, use grok patterns with error handling.
Configure Kafka as an ingestion buffer between data sources and Logstash to handle ingestion spikes and enable replayability.
Choose between Filebeat lightweight shipping and Logstash heavy transformation based on CPU constraints at the edge.
Validate timestamp extraction logic in Logstash to prevent misalignment in time-based indices due to timezone or format mismatches.
Set up conditional pipelines in Logstash to route data by type (e.g., application logs vs. audit trails) before indexing.

Module 2: Index Design and Sharding Strategy

Determine primary shard count at index creation based on anticipated data volume and node count, knowing it cannot be changed later.
Size shards between 10–50 GB to balance search performance and cluster management overhead.
Implement time-based index naming (e.g., logs-2024-04-01) to support index lifecycle management and faster deletions.
Use index templates to enforce consistent mappings, settings, and shard allocation across dynamically created indices.
Allocate replica shards considering availability requirements versus storage cost, especially in multi-zone deployments.
Prevent shard sprawl by consolidating low-volume data streams using data streams and ILM rollover policies.
Adjust refresh_interval per index based on search latency requirements—lower for real-time dashboards, higher for batch logs.

Module 3: Mapping and Schema Management

Define explicit field mappings for high-cardinality fields (e.g., user IDs) to avoid dynamic mapping explosions.
Use keyword and text field types appropriately: keyword for aggregations/filters, text for full-text search.
Disable _all field and limit field expansion in mappings to reduce indexing overhead and index size.
Set norms: false on fields not used in scoring to save disk and memory in large indices.
Implement index templates with dynamic templates to auto-apply settings based on field name patterns (e.g., *ip → ip type).
Freeze index mappings after stabilization to prevent unintended schema drift from application changes.
Monitor mapping conflicts in multi-pipeline environments where different sources write to the same index pattern.

Module 4: Index Lifecycle Management (ILM)

Define ILM policies with hot, warm, cold, and delete phases aligned to data access patterns and compliance requirements.
Trigger rollover based on index size or age, ensuring no single index exceeds performance thresholds.
Rebalance indices from hot to warm nodes by updating shard allocation filters after rollover.
Freeze cold indices to reduce JVM heap usage while retaining searchability for audit access.
Set up forcemerge and shrink operations in the warm phase for large read-only indices.
Automate snapshot creation before the delete phase for regulatory retention and disaster recovery.
Monitor ILM explain API to troubleshoot failed transitions and policy violations.

Module 5: Data Stream Architecture and Management

Convert time-series indices to data streams to simplify management of write indices and rollover behavior.
Configure data stream templates with matching index templates to enforce settings across backing indices.
Use _data_stream API to monitor active write indices and detect ingestion bottlenecks.
Manage privileges for data stream operations, ensuring producers can only write to allowed streams.
Integrate data streams with Fleet-managed agents to standardize telemetry collection.
Handle schema changes in data streams by updating the matching index template and rolling over to a new backing index.
Monitor backing index count per data stream to avoid exceeding cluster-level index limits.

Module 6: Performance Optimization During Indexing

Tune bulk request size and frequency in Logstash output to maximize throughput without triggering circuit breakers.
Adjust thread_pool.write.queue_size on data nodes to buffer indexing load during peak ingestion.
Disable refresh during bulk imports using ?refresh=false and restore afterward to accelerate indexing.
Use _bulk API with proper error handling instead of individual index requests in custom applications.
Preprocess and drop unnecessary fields in Logstash to reduce network and disk usage.
Implement backoff and retry logic in clients to handle 429 (Too Many Requests) responses gracefully.
Monitor indexing pressure metrics to detect sustained high load and adjust node resources.

Module 7: Security and Access Control for Indices

Define role-based index privileges (read, write, delete) using Elasticsearch roles and map to LDAP/AD groups.
Apply field- and document-level security to restrict sensitive data exposure in shared indices.
Enable index encryption at rest for compliance with data protection regulations (e.g., GDPR, HIPAA).
Use index aliases with restricted permissions to expose only relevant data to specific teams.
Rotate API keys used for indexing pipelines on a scheduled basis and audit key usage.
Log and monitor unauthorized index creation attempts using audit logging and watcher alerts.
Isolate indices by tenant in multi-customer environments using index patterns and role wildcards.

Module 8: Monitoring, Alerting, and Index Health

Track index growth rate using Kibana or custom queries to anticipate storage and shard allocation issues.
Set up alerts for high indexing latency, shard failures, or unassigned replicas using Elasticsearch watcher.
Use _cat APIs and Kibana Stack Monitoring to identify hot shards and rebalance uneven loads.
Regularly audit index settings for deviations from organizational standards using ILM or scripts.
Monitor merge throttling and disk I/O to detect indexing bottlenecks on data nodes.
Validate snapshot integrity for indices containing critical data using periodic restore tests.
Correlate indexing errors in Logstash logs with Elasticsearch cluster logs to isolate root causes.

Module 9: Disaster Recovery and Index Restoration

Configure repository locations for snapshots with access controls and cross-cluster replication.
Test full cluster and individual index restores from snapshots to validate recovery time objectives.
Use partial restores to recover specific indices without overwriting healthy cluster state.
Replicate critical indices to a separate cluster in another region using cross-cluster search or replication.
Document and version control index templates and ILM policies to ensure consistency after rebuilds.
Plan for UUID mismatches when restoring indices to different clusters by using alias-based references.
Automate snapshot deletion based on retention policies to prevent unbounded storage growth.