ELK

ELK is an acronym that refers to a stack of three open-source software products: Elasticsearch, Logstash, and Kibana. These tools are commonly used for log and data analytics, making it easier to search, analyze, and visualize large volumes of data generated by various systems and applications.


This competency area includes an understanding of log management, elasticsearch query language, data modeling, indexing and sharding, Kibana visualization, data transformation, pipeline management, cluster management, security and access control, performance tuning, scaling and high availability, monitoring and alerting, scripting and automation, troubleshooting and integration.
 

Key Competencies:

  1. Log Management - Understanding how to collect, parse, and manage log data from various sources. Configuring Logstash to ingest logs, applying grok patterns for parsing, and managing log rotation and retention policies.

  2. Elasticsearch Query Language - Knowledge of Elasticsearch Query DSL for retrieving relevant information from large datasets.

  3. Data Modeling - Ability to design effective data models and document structures in Elasticsearch for efficient data retrieval and analysis. This includes understanding data types, mapping, and indexing strategies.

  4. Indexing and Sharding - Ability to manage indexes and shards.

  5. Kibana Visualization - Proficiency in creating visualizations and dashboards in Kibana using visualization tools like Charts, Maps, and Timelion.

  6. Data Transformation - Familiarity with the filters and knowledge of using them for data enrichment and transformation.

  7. Pipeline Management - Managing Logstash pipelines, configuring inputs and outputs, and ensuring data flows smoothly through the pipeline.

  8. Elasticsearch Cluster Management - Understanding how to set up and manage Elasticsearch clusters, including node configuration, cluster health monitoring, and scaling.

  9. Security and Access Control - Implementing security measures such as authentication, authorization, and encryption in both Elasticsearch and Kibana to protect sensitive data and ensure proper access control.

  10. Performance Tuning - Ability to identify and address performance bottlenecks, optimize queries, and fine-tune Elasticsearch and Logstash for efficient data processing.

  11. Scaling and High Availability -Knowledge of the strategies for scaling ELK stack components horizontally and ensuring high availability and fault tolerance.

  12. Monitoring and Alerting Implementing monitoring solutions to keep track of ELK stack performance and setting up alerts for critical events.

  13. Scripting and Automation - Writing scripts and automation routines for managing and deploying ELK stack components.

  14. Troubleshooting - Diagnosing and resolving issues related to data ingestion, indexing, querying, and visualization.

  15. Integration - Integrating ELK with other systems, applications, and third-party tools as part of a broader monitoring and analytics ecosystem.