Description

This curriculum spans the equivalent of a multi-workshop operational immersion, covering the design, deployment, and day-to-day management of ELK Stack querying in production environments, comparable to an internal engineering enablement program for platform or observability teams.

Module 1: Architecture and Component Roles in the ELK Stack

Selecting between Logstash and Beats based on data ingestion throughput and transformation requirements
Configuring Elasticsearch shard allocation to balance query performance and cluster resource utilization
Deciding on co-locating Kibana with Elasticsearch or deploying it separately for security and scalability
Designing index lifecycle management policies to automate rollover and deletion based on retention SLAs
Choosing between hot-warm-cold architectures versus flat clusters based on query latency and cost constraints
Implementing dedicated master and ingest nodes to isolate cluster management from data processing workloads
Evaluating the impact of using ingest pipelines versus pre-processing data in Logstash
Planning for high availability by configuring minimum master nodes and shard replication settings

Module 2: Data Ingestion and Pipeline Design

Mapping incoming log formats to appropriate Logstash filters (grok, dissect, json) based on structure and performance
Handling multiline logs (e.g., Java stack traces) using multiline codec configurations in Filebeat or Logstash
Optimizing pipeline throughput by batching events and tuning worker threads in Logstash
Validating field data types during ingestion to prevent mapping conflicts in Elasticsearch
Implementing conditional parsing logic in pipelines to route or modify data based on source or content
Securing data in transit using TLS between Beats, Logstash, and Elasticsearch
Managing pipeline versioning and deployment using CI/CD for configuration consistency
Handling pipeline backpressure by monitoring queue depths and adjusting input rates

Module 3: Index Design and Mapping Strategies

Defining custom index templates with appropriate mappings to enforce data types and avoid dynamic mapping issues
Selecting keyword vs. text data types based on query patterns (exact match vs. full-text search)
Configuring index settings such as refresh interval and number of replicas for write-heavy versus read-heavy workloads
Using aliases to abstract physical indices and support seamless rollovers in time-based indices
Designing index naming conventions that support retention policies and routing queries efficiently
Disabling _source for specific indices when storage is constrained and retrieval is not required
Implementing nested and object data types based on document complexity and query needs
Setting up index-level access controls using role-based privileges in conjunction with index patterns

Module 4: Querying Data with Kibana Discover and Lens

Constructing time-based queries in Discover with precise time range selections aligned to business SLAs
Using field filters to isolate high-cardinality fields that impact query performance
Creating and saving reusable search objects with parameterized filters for team consistency
Interpreting relevance scoring in full-text searches to assess result accuracy
Configuring default index patterns in Kibana to match active data streams
Optimizing field formatting in Discover to ensure correct display of dates, IP addresses, and numeric values
Diagnosing missing data in Discover by validating index pattern time filters and index existence
Using pinned queries and time locks to maintain context during incident investigations

Module 5: Advanced Query DSL and Performance Optimization

Writing compound queries using bool (must, should, must_not, filter) to express complex business logic
Selecting term vs. match queries based on exact value matching versus analyzed text search
Using query context versus filter context to leverage caching and improve performance
Limiting result sets with from/size and using search_after for deep pagination without performance degradation
Profiling slow queries using the Profile API to identify costly clauses and rewrite them
Applying source filtering to retrieve only required fields and reduce network overhead
Using aggregations with size limits to prevent high-cardinality field explosions
Implementing index sorting to optimize range queries and reduce document scoring overhead

Module 6: Aggregations for Operational and Business Insights

Choosing metric aggregations (avg, sum, cardinality) based on data semantics and accuracy requirements
Configuring date histogram intervals that align with data granularity and visualization needs
Using pipeline aggregations to calculate derivatives, moving averages, and cumulative sums
Handling high-cardinality terms aggregations with sampling or composite aggregations to avoid timeouts
Nesting aggregations to generate multi-dimensional reports (e.g., error count by service and region)
Setting shard_size in terms aggregations to improve accuracy at the cost of performance
Validating aggregation results against raw data samples to detect bucket inaccuracies
Using the sampler aggregation to improve performance on large datasets with acceptable precision loss

Module 7: Security, Access Control, and Audit Logging

Configuring role-based access control (RBAC) to restrict index and feature access in Kibana
Implementing field-level security to mask sensitive data (e.g., PII) in query results
Setting up document-level security to limit data visibility based on user roles or teams
Enabling audit logging in Elasticsearch to track authentication, index access, and configuration changes
Integrating with LDAP or SAML for centralized user identity management
Rotating API keys and service account credentials on a defined schedule
Validating TLS configurations across all ELK components to prevent downgrade attacks
Monitoring for unauthorized changes using audit trail analysis and alerting rules

Module 8: Monitoring, Alerting, and Anomaly Detection

Creating threshold-based alerts in Kibana Alerting for log volume spikes or error rate increases
Configuring alert actions with rate limiting to prevent notification storms
Using machine learning jobs in Elastic Stack to detect anomalies in time series data
Tuning anomaly detection models by adjusting bucket spans and function types
Validating alert conditions against historical data to reduce false positives
Monitoring Elasticsearch cluster health and query latency using Metricbeat and prebuilt dashboards
Setting up index threshold monitors for disk usage and shard count to prevent outages
Integrating alert outputs with external systems (e.g., PagerDuty, Slack) using webhooks

Module 9: Production Operations and Troubleshooting

Diagnosing slow queries by analyzing profile output and identifying inefficient filters or aggregations
Resolving mapping conflicts by reindexing data with corrected templates and aliases
Recovering from index corruption using snapshot and restore procedures from a verified backup
Scaling cluster capacity by adding data nodes and rebalancing shards without downtime
Managing index growth by enforcing ILM policies and monitoring rollover triggers
Investigating data loss by tracing pipeline logs from Beats through Logstash to Elasticsearch
Updating ELK components in a rolling fashion to maintain availability during version upgrades
Validating backup integrity by restoring snapshots to a test environment on a recurring schedule