Description

This curriculum spans the design and operational rigor of a multi-workshop program, covering the breadth of decisions and trade-offs involved in building and maintaining enterprise-grade data pipelines within the ELK Stack, comparable to those encountered in large-scale logging infrastructures and internal platform engineering initiatives.

Module 1: Understanding Data Sources and Ingestion Patterns

Evaluate log file rotation strategies and their impact on Filebeat’s harvesting continuity and file state tracking.
Configure multiline log event handling in Filebeat for stack traces without over-aggregating unrelated entries.
Select between Logstash and Beats for ingestion based on transformation needs, resource constraints, and pipeline complexity.
Design JSON schema expectations for application logs to ensure consistent parsing at ingestion.
Implement file ownership and permissions policies for log directories to enable non-root Beat operation.
Assess the trade-offs of pushing parsing logic to clients (e.g., structured logging) versus centralizing in Logstash.
Integrate syslog inputs in Logstash while managing message truncation and RFC compliance.
Monitor ingestion lag across distributed Filebeat instances using internal metrics and heartbeat events.

Module 2: Logstash Pipeline Architecture and Performance

Partition Logstash configuration files by function (inputs, filters, outputs) to support team collaboration and version control.
Tune pipeline workers and batch sizes based on CPU core availability and event throughput requirements.
Implement conditional filtering to route events through specific grok patterns without degrading overall throughput.
Use dissect filters instead of grok for structured logs to reduce CPU overhead in high-volume pipelines.
Manage plugin dependencies and versions in production using Logstash’s plugin manager and offline bundle deployment.
Isolate high-latency outputs (e.g., external APIs) into separate pipelines to prevent backpressure on core indexing.
Configure persistent queues to survive Logstash restarts without event loss under disk space constraints.
Instrument pipeline performance using Logstash monitoring APIs to identify filter bottlenecks.

Module 3: Elasticsearch Index Design and Lifecycle Management

Define time-based index naming conventions (e.g., logs-app-2024.04.01) to support automated rollover and search patterns.
Configure index templates with appropriate dynamic mapping rules to prevent field explosion from unstructured data.
Set shard counts based on data volume, retention period, and cluster node count to balance query performance and overhead.
Implement Index Lifecycle Policies to automate rollover, force merge, and deletion of indices according to compliance rules.
Disable _source for specific indices when storage cost outweighs debuggability, accepting the loss of reindexing flexibility.
Use runtime fields to compute values at query time for infrequently accessed derived data without indexing overhead.
Prevent mapping conflicts by enforcing strict field type definitions in index templates for shared environments.
Estimate storage growth using historical ingestion rates and compression ratios to plan cluster capacity.

Module 4: Data Parsing and Transformation Techniques

Develop custom grok patterns for proprietary log formats and validate them against edge cases using sample datasets.
Handle timestamp parsing from multiple time zones and formats using conditional date filters in Logstash.
Normalize IP addresses and user agent strings into structured fields for consistent querying and analysis.
Extract nested JSON payloads from string fields using the json filter and manage schema drift with on_error handling.
Mask sensitive data (e.g., credit card numbers) during ingestion using mutate filters and regex patterns.
Enrich events with geographic data using Logstash’s geoip filter and manage database update schedules.
Flatten deeply nested structures to comply with Elasticsearch’s object field limitations and improve query performance.
Validate transformation logic using Logstash’s ruby debug output before deploying to production.

Module 5: Field Mapping and Schema Governance

Adopt ECS (Elastic Common Schema) field names to standardize event data across teams and tools.
Define custom field types (e.g., flattened, keyword, text) based on query patterns and aggregation needs.
Reserve high-cardinality fields as keyword with ignore_above to prevent indexing of problematic values.
Coordinate schema changes across ingestion, indexing, and visualization layers using change control procedures.
Document field definitions and ownership in a centralized schema registry accessible to data producers.
Handle schema versioning by introducing new fields rather than modifying existing mappings to maintain backward compatibility.
Audit field usage in Kibana dashboards to identify deprecated or redundant fields for cleanup.
Restrict dynamic mapping for specific indices to prevent unintended field creation from malformed input.

Module 6: Security and Access Control in Data Flows

Configure TLS between Beats and Logstash/Elasticsearch to encrypt data in transit across network zones.
Implement role-based access control in Elasticsearch to restrict index read/write permissions by team and environment.
Use ingest node pipelines to redact sensitive fields before indexing based on user or application context.
Integrate LDAP/Active Directory with Kibana to enforce enterprise authentication and group-based access.
Enable audit logging in Elasticsearch to track configuration changes and data access by user and IP.
Mask field values in Kibana discover views for non-privileged roles using field-level security.
Rotate API keys and service account credentials on a defined schedule using automation scripts.
Validate input payloads in Logstash to prevent Elasticsearch query injection via malicious field content.

Module 7: Monitoring, Alerting, and Operational Health

Deploy Metricbeat on Elasticsearch nodes to collect JVM, thread pool, and filesystem metrics for capacity planning.
Create alerts in Kibana for sustained high indexing latency or shard relocation events.
Monitor Logstash filter performance to detect grok pattern inefficiencies causing CPU spikes.
Track Filebeat registry file size and offset consistency to detect harvesting stalls.
Use Elasticsearch’s _cluster/allocation/explain API to diagnose unassigned shards after node failure.
Set up dead letter queues in Logstash for failed events and define remediation procedures.
Baseline normal ingestion rates and trigger alerts on deviations indicating source or pipeline issues.
Validate backup integrity by restoring snapshot to a test cluster on a quarterly schedule.

Module 8: Scalability and High Availability Design

Distribute ingest load across multiple Logstash instances using load balancers and consistent hashing.
Configure Elasticsearch ingest nodes separately from data nodes to isolate parsing resource consumption.
Implement cross-cluster replication for critical indices to support disaster recovery requirements.
Size Elasticsearch master-eligible nodes to avoid split-brain scenarios in multi-zone deployments.
Use coordinator-only nodes to handle client traffic and reduce load on data and master nodes.
Plan shard rebalancing thresholds to prevent excessive network traffic during routine operations.
Test cluster behavior under node failure by simulating network partitions and power loss.
Deploy Filebeat in Kubernetes as a DaemonSet with proper log path mounting and resource limits.

Module 9: Integration with External Systems and Compliance

Forward curated event streams to SIEM platforms using Elasticsearch output plugins or Kafka integration.
Export data subsets for regulatory audits using Elasticsearch’s _search API with scroll context.
Implement data retention policies aligned with GDPR or HIPAA requirements using ILM and field masking.
Integrate with SOAR platforms by triggering alerts from Kibana into incident response workflows.
Validate log integrity using cryptographic hashing at ingestion and store hashes in immutable storage.
Document data lineage from source to index for compliance audits, including transformation steps.
Configure anonymization pipelines for test environments using synthetic data or masked production extracts.
Support eDiscovery requests by preserving specific indices beyond standard retention periods with immutable settings.