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ELK Stack in ELK Stack

ELK Stack in ELK Stack

$247.00
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Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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What does the ELK Stack in ELK Stack course cover?

ELK Stack in ELK Stack is covered here in 8 modules: Architecture Design and Cluster Topology Planning, Data Ingestion Pipeline Engineering, Index Management and Data Lifecycle Policies and 5 more. The outline lists 48 specific topics, opening with selecting node roles (master, data, ingest, coordinating) based on workload patterns and fault tolerance requirements.

How do you approach ELK Stack in ELK Stack step by step?

The work is sequenced in 8 stages. It starts with Architecture Design and Cluster Topology Planning, moves through Data Ingestion Pipeline Engineering and Index Management and Data Lifecycle Policies, and ends at Scaling and Upgrade Operations. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the ELK Stack in ELK Stack course?

Module 1 is Architecture Design and Cluster Topology Planning. It works through selecting node roles (master, data, ingest, coordinating) based on workload patterns and fault tolerance requirements., designing multi-zone Elasticsearch cluster layouts to meet high availability SLAs while minimizing cross-zone network costs., calculating shard allocation per index to balance query performance against cluster overhead and recovery time. and 3 more.

How is the ELK Stack in ELK Stack course delivered?

The ELK Stack in ELK Stack course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the ELK Stack in ELK Stack course cost?

The ELK Stack in ELK Stack course is $248 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: ELK Stack Toolkit, Elasticsearch in ELK Stack, Full Stack Monitoring in ELK Stack, Log Searching in ELK Stack.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the technical breadth of a multi-phase infrastructure rollout, covering the same operational rigor found in enterprise search platform deployments, from initial cluster design through ongoing lifecycle management and large-scale resilience planning.

Module 1: Architecture Design and Cluster Topology Planning

  • Selecting node roles (master, data, ingest, coordinating) based on workload patterns and fault tolerance requirements.
  • Designing multi-zone Elasticsearch cluster layouts to meet high availability SLAs while minimizing cross-zone network costs.
  • Calculating shard allocation per index to balance query performance against cluster overhead and recovery time.
  • Implementing dedicated ingest nodes to offload processing from data nodes under heavy indexing loads.
  • Deciding between single-cluster and cross-cluster search architectures for multi-tenant environments.
  • Planning for index lifecycle rollover strategies during initial cluster design to support long-term data retention.

Module 2: Data Ingestion Pipeline Engineering

  • Configuring Logstash pipelines with conditional filters to parse heterogeneous log formats from multiple sources.
  • Tuning Beats buffer sizes and acknowledgment settings to prevent data loss during network interruptions.
  • Implementing dead-letter queues in Kafka to capture failed events during Logstash processing.
  • Choosing between Filebeat lightweight modules and custom Logstash configurations based on parsing complexity.
  • Securing data in transit between Beats and Logstash using mutual TLS with internal PKI.
  • Managing pipeline backpressure by adjusting batch sizes and worker threads in high-throughput scenarios.

Module 3: Index Management and Data Lifecycle Policies

  • Defining ILM policies that transition indices from hot to warm nodes based on age and access frequency.
  • Setting up rollover triggers based on index size or age to prevent oversized primary shards.
  • Configuring shrink and force merge operations during off-peak hours to reduce storage footprint.
  • Implementing data retention policies that comply with regulatory requirements for log deletion.
  • Managing alias transitions during index rollover to maintain application query continuity.
  • Monitoring index write rates to preemptively adjust shard counts before rollover.

Module 4: Search Optimization and Query Performance Tuning

  • Choosing between keyword and text field types based on exact match versus full-text search needs.
  • Designing custom analyzers for domain-specific log data such as application traces or firewall rules.
  • Using _source filtering to reduce network payload in high-frequency monitoring dashboards.
  • Implementing query caching strategies for frequently accessed time-series dashboards.
  • Tuning refresh intervals on time-based indices to balance search latency and indexing throughput.
  • Diagnosing slow queries using the Profile API and rewriting aggregations to reduce bucket counts.

Module 5: Security Configuration and Access Control

  • Mapping LDAP/AD groups to Elasticsearch roles to enforce least-privilege access across teams.
  • Configuring index-level permissions to restrict SOC analysts from modifying production indices.
  • Enabling audit logging to track configuration changes and unauthorized access attempts.
  • Rotating TLS certificates for internode and client communication on a defined schedule.
  • Implementing API key management for service accounts used by monitoring tools.
  • Validating role-based access through automated integration tests after security policy updates.

Module 6: Monitoring, Alerting, and Cluster Health Management

  • Setting up metricbeat to monitor JVM heap usage and thread pool rejections on data nodes.
  • Configuring Kibana alert conditions for sustained high disk watermark breaches.
  • Creating custom dashboards to track indexing latency and search error rates over time.
  • Integrating Elasticsearch cluster alerts with PagerDuty or Opsgenie via webhook actions.
  • Using the Cat API in automated scripts to detect unassigned shards after node failures.
  • Establishing baseline performance metrics during normal operations for anomaly detection.

Module 7: Backup, Recovery, and Disaster Resilience

  • Registering shared file system or S3 repositories for snapshot storage with access controls.
  • Scheduling incremental snapshots aligned with ILM delete phases to avoid orphaned data.
  • Testing restore procedures on isolated clusters to validate snapshot integrity quarterly.
  • Replicating critical indices to a secondary region using cross-cluster replication for DR.
  • Documenting recovery runbooks that specify restore order for interdependent indices.
  • Calculating RPO and RTO based on snapshot frequency and measured restore times.

Module 8: Scaling and Upgrade Operations

  • Planning rolling upgrades with version compatibility checks for Beats, Logstash, and Kibana.
  • Adding capacity via cold nodes before reassigning indices to maintain query performance.
  • Executing shard rebalancing after node additions while respecting allocation filters.
  • Migrating from deprecated features such as mapping types before major version upgrades.
  • Validating plugin compatibility with new Elasticsearch versions in staging environments.
  • Coordinating maintenance windows with application teams to minimize impact during cluster restarts.
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