Kibana Test

This skill assesses the candidate's ability to create, modify, and optimize a wide range of visualizations using Elasticsearch data. It covers foundational visualizations such as bar and line charts, moving to more advanced options like heat maps, pie charts, and area charts. Candidates are evaluated on their understanding of how visualizations work with Elasticsearch queries and their ability to combine multiple visualizations into comprehensive dashboards.

This skill evaluates the candidate’s ability to build and execute queries using the Kibana Query Language (KQL). It tests knowledge of KQL syntax, operators, and filters, including the ability to create complex queries for extracting, manipulating, and visualizing data. Proficiency in using KQL for both simple and advanced queries is crucial for dynamic data exploration and filtering.

This skill tests the candidate’s skills in creating, managing, and optimizing Kibana dashboards. It covers building interactive and user-friendly dashboards using various visualizations, configuring interactive filters, and cross-filtering across different panels. Candidates are expected to demonstrate best practices for organizing, sharing, and securing dashboards in multi-user environments.

Candidates are evaluated on their ability to use Kibana’s advanced tools like Timelion and Canvas. Timelion is used for time-series data analysis, while Canvas allows for the creation of highly customized visualizations and infographics. Mastery of these tools is critical for creating dynamic, data-driven visual content that goes beyond standard dashboards.

This skill assesses understanding of Elasticsearch index patterns and how Kibana interacts with these patterns to structure and present data. It involves defining and managing index patterns, configuring time filters, and managing multiple Elasticsearch indices within Kibana. Knowledge of Elasticsearch field types and mappings is essential for efficient data access and visualization.

This skill evaluates the candidate’s knowledge of Kibana’s machine learning capabilities, focusing on anomaly detection, forecasting, and creating machine learning jobs for predictive analysis. Additionally, candidates are tested on setting up Kibana alerts to monitor critical data and respond to predefined thresholds in real-time.

This skill assesses the candidate's ability to optimize both Kibana and Elasticsearch for performance in high-demand environments. It focuses on query optimization techniques and strategies for caching, shard allocation, and optimizing Elasticsearch clusters to ensure that Kibana remains responsive and performant with large datasets.

This skill evaluates the candidate’s knowledge of securing Kibana and Elasticsearch environments, including managing role-based access control, configuring authentication, and setting up encryption protocols. Candidates must demonstrate the ability to implement security best practices and manage configurations at scale.

This skill focuses on the candidate’s ability to architect and deploy Kibana solutions in enterprise-level environments. It covers multi-cluster setups, custom plugin development, and strategies for scaling Kibana solutions for large datasets and high-query workloads. Proficiency in Elasticsearch tuning is essential to ensure Kibana's performance and reliability in massive-scale operations.