Description

This curriculum spans the technical breadth of a multi-workshop program on ELK data ingestion, covering architecture planning, pipeline optimization, security hardening, and scalability engineering comparable to an internal capability build for large-scale log management.

Module 1: Understanding ELK Stack Architecture and Data Flow

Evaluate the role of each ELK component (Elasticsearch, Logstash, Kibana) in handling data ingestion and determine which components are mandatory based on data source and use case.
Design cluster topology (data, ingest, master nodes) to support expected data volume and query load during import operations.
Configure network ports and firewall rules to allow secure communication between Beats, Logstash, and Elasticsearch nodes.
Select appropriate transport protocols (HTTP, TCP, TLS) for data transmission based on security and performance requirements.
Assess the impact of sharding and replication settings on ingestion throughput and recovery time during bulk imports.
Implement health checks and monitoring for each ELK service to detect failures during data import pipelines.
Determine whether to use centralized Logstash or lightweight Beats based on resource constraints and data preprocessing needs.

Module 2: Data Source Identification and Classification

Classify data sources by structure (structured, semi-structured, unstructured) and update frequency (real-time, batch, event-driven) to inform ingestion strategy.
Map application logs, system metrics, and database exports to appropriate Beats (Filebeat, Metricbeat, Auditbeat) or custom Logstash inputs.
Identify sensitive data elements (PII, credentials) during source analysis to enforce early-stage masking or filtering.
Document schema expectations and field naming conventions per data source to ensure consistency across indices.
Assess log rotation policies on source systems to configure Filebeat harvesting settings (close_inactive, clean_inactive).
Validate timestamp formats across heterogeneous sources to prevent misalignment in Kibana time-series views.
Inventory third-party APIs and their rate limits when planning data pull intervals via Logstash HTTP input.

Module 3: Logstash Pipeline Configuration and Optimization

Structure Logstash configuration files into input, filter, and output sections with conditional logic for multi-source pipelines.
Use mutate and dissect filters to parse unstructured logs instead of grok when performance is critical and patterns are predictable.
Configure pipeline workers and batch sizes based on CPU core count and input throughput to avoid backpressure.
Implement dead-letter queues for failed events to enable post-failure analysis without data loss.
Use persistent queues on disk to prevent data loss during Logstash restarts or crashes.
Minimize filter complexity by offloading enrichment (e.g., GeoIP lookups) to ingest nodes in Elasticsearch when feasible.
Test pipeline performance using Logstash’s --config.test_and_exit and benchmark with sample production data.

Module 4: Filebeat and Metricbeat Deployment Strategies

Configure Filebeat prospectors to monitor specific log paths and exclude irrelevant files using ignore_older and close_eof settings.
Enable TLS encryption between Filebeat and Logstash or Elasticsearch to meet compliance requirements for data in transit.
Use Filebeat modules for common services (Nginx, MySQL) to leverage prebuilt parsers and dashboards, then customize as needed.
Set up Metricbeat to collect system and service metrics at defined intervals, adjusting period and metricsets per host load.
Manage Filebeat registry file size and cleanup to prevent disk exhaustion on long-running hosts.
Deploy Beats using configuration management tools (Ansible, Puppet) for consistent rollout across large fleets.
Configure output load balancing and failover to multiple Logstash instances or Elasticsearch nodes.

Module 5: Schema Design and Index Management

Define custom index templates with appropriate mappings to enforce data types and avoid dynamic mapping issues.
Use index aliases to decouple applications from physical index names, enabling rollover and reindexing operations.
Implement Index Lifecycle Management (ILM) policies to automate rollover, shrink, and deletion based on size or age.
Set up time-based indices (e.g., logs-2024-04-01) with daily or weekly rotation aligned with retention policies.
Prevent field mapping conflicts by validating new data against existing templates before full deployment.
Optimize keyword vs. text field usage in mappings based on search and aggregation requirements.
Estimate shard count per index based on data volume and retention to avoid oversized or undersized shards.

Module 6: Data Transformation and Enrichment

Use Logstash mutate filters to rename, remove, or convert fields to align with organizational naming standards.
Integrate external reference data (e.g., IP-to-location, user lookup tables) using Logstash JDBC or CSV filters.
Apply conditional filtering to drop irrelevant events (e.g., health checks, 200 status codes) before indexing.
Normalize timestamps into @timestamp field using date filters with multiple format fallbacks.
Flatten nested JSON structures to improve query performance and reduce index overhead.
Mask or remove sensitive fields (e.g., credit card numbers) using gsub or ruby filters prior to transmission.
Enrich events with static metadata (environment, region, team) using Logstash add_field directives.

Module 7: Security and Access Control in Data Ingestion

Configure Elasticsearch API keys or service accounts for Beats and Logstash instead of shared user credentials.
Enable Role-Based Access Control (RBAC) to restrict index creation and write permissions to specific ingestion roles.
Encrypt configuration files containing passwords using Elasticsearch keystore and reference values via ${} syntax.
Validate certificate chains when using TLS between Beats and Logstash to prevent man-in-the-middle attacks.
Audit ingestion pipeline changes using version control and change management processes.
Restrict Logstash plugin installations to approved sources to prevent malicious code execution.
Monitor for unauthorized index creation attempts or spikes in document ingestion rates.

Module 8: Monitoring, Troubleshooting, and Performance Tuning

Instrument Logstash with monitoring APIs to track event throughput, queue depth, and filter performance.
Analyze Elasticsearch ingest node CPU and memory usage to identify bottlenecks in pipeline processing.
Use Kibana’s Stack Monitoring to correlate ingestion delays with cluster health and resource saturation.
Interpret Filebeat logging output to diagnose harvester and publisher errors during log collection.
Adjust Logstash pipeline batch size and workers when event processing latency exceeds SLA thresholds.
Diagnose backpressure by examining Elasticsearch thread pool rejections and queue sizes.
Use Elasticsearch _bulk API response codes to detect indexing failures and implement retry logic.

Module 9: Scalability and High Availability Planning

Deploy multiple Logstash instances behind a load balancer to distribute ingestion load and eliminate single points of failure.
Configure Filebeat to use load-balanced outputs with sticky connections to maintain event ordering when required.
Size Elasticsearch ingest nodes separately from data nodes to isolate processing impact.
Plan for regional data collection by deploying edge Logstash instances and aggregating to central clusters.
Test failover scenarios by simulating Logstash node outages and verifying Beats’ retry behavior.
Scale index shard count and replica settings based on projected write volume and read query concurrency.
Implement automated pipeline deployment using CI/CD to ensure configuration consistency across environments.