Data Streaming in ELK Stack

This curriculum spans the design and operational rigor of a multi-workshop program focused on enterprise-grade data streaming, comparable to an internal capability build for managing complex, production-scale ELK and Kafka deployments across distributed environments.

Module 1: Architecting Scalable Data Ingestion Pipelines

• Designing Logstash configurations with conditional filters to route high-volume logs from heterogeneous sources without performance degradation
• Selecting between Beats and Logstash forwarders based on resource constraints and parsing requirements at the edge
• Implementing backpressure handling in Kafka-to-Logstash pipelines to prevent data loss during Elasticsearch indexing delays
• Configuring multi-stage ingestion pipelines with dead-letter queues for failed event inspection and reprocessing
• Optimizing TCP buffer sizes and connection timeouts in Filebeat to handle intermittent network connectivity in remote data centers
• Partitioning incoming streams by tenant or geography in Kafka to enable isolated processing and compliance boundaries
• Deploying sidecar agents in Kubernetes to capture container logs without impacting application pod performance
• Choosing between JSON and raw text log formats at ingestion based on downstream parsing cost and query flexibility

Module 2: Real-Time Stream Processing with Logstash

• Writing custom Ruby filters in Logstash to enrich streaming events with real-time threat intelligence feeds
• Managing JVM heap allocation for Logstash workers processing high-cardinality fields to avoid garbage collection stalls
• Implementing throttling mechanisms in filter pipelines to prevent downstream overload during traffic spikes
• Using mutate and date filters to standardize timestamp formats across disparate systems before indexing
• Configuring persistent queues in Logstash to survive process restarts without re-ingesting from source
• Isolating pipeline stages using separate config files to simplify debugging and version control
• Applying conditional filtering to mask sensitive fields (e.g., PII) before events leave the ingestion layer
• Integrating external APIs in filters for dynamic enrichment while managing timeout and retry logic

Module 3: Kafka Integration for Buffering and Orchestration

• Defining topic retention policies based on compliance requirements and storage budget constraints
• Configuring replication factors and partition counts to balance fault tolerance and parallel processing throughput
• Setting up MirrorMaker for cross-data-center replication of critical log streams
• Managing consumer group offsets to prevent data reprocessing or loss during Logstash restarts
• Securing Kafka brokers with SSL/TLS and SASL authentication for regulated environments
• Monitoring lag in consumer groups to detect indexing bottlenecks before data loss occurs
• Using compacted topics for configuration change streams where only the latest state is relevant
• Implementing schema validation via Schema Registry to enforce consistency across producers

Module 4: Elasticsearch Indexing and Data Modeling

• Designing time-based index templates with ILM policies to automate rollover and retention
• Choosing appropriate shard counts per index to balance query performance and cluster overhead
• Mapping high-cardinality fields as keyword with doc_values disabled to reduce memory footprint
• Configuring dynamic templates to handle unknown fields from evolving data sources
• Using nested and flattened data types to model hierarchical log structures without explosion
• Preventing mapping conflicts by enforcing strict schema discipline at ingestion
• Setting up index aliases to enable zero-downtime reindexing and schema migrations
• Optimizing refresh intervals for time-series indices to balance search latency and indexing throughput

Module 5: Performance Tuning and Cluster Sizing

• Right-sizing data node memory to cap heap at 32GB while maximizing off-heap caching
• Allocating dedicated ingest nodes to isolate parsing load from search and storage functions
• Configuring thread pools for bulk, search, and indexing to prevent queue saturation under load
• Using shard allocation filtering to place hot indices on SSD-backed nodes and cold data on HDD
• Monitoring file system cache hit ratios to identify undersized nodes or inefficient queries
• Adjusting bulk request sizes in Logstash to maximize throughput without triggering circuit breakers
• Implementing circuit breaker limits for fielddata and request memory to protect cluster stability
• Planning cluster growth using historical ingestion rates and retention policies

Module 6: Security and Access Governance

• Enabling TLS encryption between Beats, Logstash, and Elasticsearch for data in transit
• Configuring role-based access control (RBAC) to restrict Kibana dashboards by team or environment
• Implementing field-level security to mask sensitive log fields for non-administrative users
• Integrating with LDAP/Active Directory for centralized user authentication and group mapping
• Auditing administrative actions in Elasticsearch to meet compliance logging requirements
• Rotating API keys and certificates on a defined schedule using automation tools
• Applying network-level filtering to restrict Logstash input plugins to trusted source IPs
• Encrypting at-rest indices using Elasticsearch’s transparent data encryption (TDE) feature

Module 7: Monitoring and Observability of Streaming Pipelines

• Instrumenting Logstash with monitoring APIs to track event throughput, queue depth, and filter performance
• Setting up Elasticsearch cluster health alerts for red/yellow states and shard relocation issues
• Using Kibana’s Monitoring UI to correlate node resource usage with ingestion spikes
• Creating custom dashboards to visualize end-to-end pipeline latency from source to index
• Configuring slow log thresholds for search and indexing to identify performance regressions
• Exporting internal metrics to a separate monitoring cluster to avoid self-interference
• Alerting on Kafka consumer lag exceeding predefined SLA thresholds
• Validating data completeness by comparing source counts with indexed document totals

Module 8: Resilience, Disaster Recovery, and Failover

• Designing multi-zone Elasticsearch clusters to survive data center outages
• Implementing automated snapshot policies to S3-compatible storage for point-in-time recovery
• Testing restore procedures from snapshots to validate RTO and RPO targets
• Configuring Filebeat to spool events to disk when Elasticsearch is unreachable
• Deploying redundant Logstash instances behind load balancers to eliminate single points of failure
• Using Kafka’s replication to buffer logs during extended Elasticsearch maintenance windows
• Documenting and testing failover procedures for cross-region log aggregation
• Validating backup integrity by restoring to isolated test environments monthly

Module 9: Advanced Use Cases and Cross-System Integration

• Integrating Elasticsearch watcher with external ticketing systems to trigger incident response on anomaly detection
• Streaming security events to SIEM platforms via Elasticsearch output plugins with transformation
• Using Kibana Canvas to generate real-time operational reports for executive dashboards
• Feeding aggregated metrics from Elasticsearch into time-series databases for long-term trend analysis
• Correlating application logs with APM traces to diagnose performance bottlenecks
• Exporting filtered log streams to data lakes for offline machine learning workloads
• Building custom Kibana plugins to visualize domain-specific streaming data patterns
• Orchestrating pipeline updates via CI/CD to deploy config changes without downtime