Kibana Training for data analysis and visualization

This Kibana training (Data Analysis with Kibana) makes you autonomous at analyzing data: searching, visualizing, presenting, alerting, machine learning and advanced features (Lens, runtime fields, Canvas, Vega).

It relies on the Sample eCommerce and Sample web logs datasets shipped with Kibana, with hands-on labs at every step. The course prepares for the Elastic Certified Data Analyst certification.

Learning objectives

• Navigate Kibana and use Spaces, Saved Objects and Data Views.
• Search, filter and visualize data from Discover.
• Build rich visualizations with Lens and Maps.
• Design interactive dashboards with drilldowns.
• Present and share results: PDF/PNG reports, Canvas, RBAC, anonymous access.
• Detect anomalies with machine learning and AIOps.
• Implement advanced features: Lens formulas, runtime fields, Vega.
• Define and manage alerting rules.

Module 1 — Getting started

• Introduction to Kibana: the UI on top of Elasticsearch, the data journey (Beats / Agent / Logstash → Elasticsearch → Kibana), sample datasets.
• Hello, Dashboard!: home page, importing a sample dataset, opening a first dashboard.
• Your space: Spaces and scope, Data Views (indices, data streams, aliases), Saved Objects, Copy to spaces.
• Lab: create a Nova space and copy a dashboard into it.

Module 2 — Searching data

• Discover and Data Visualizer: documents, fields, values; Elasticsearch types (text vs keyword, searchable vs aggregatable), histogram and statistics.
• KQL and filters: Kibana filters vs query bar, pinning, boolean operators, saved queries vs saved searches.
• Focus on fields: visualize from the field list (Lens, Maps for geo points), top values, suggestions panel.
• Labs: query and filter logs with KQL, create visualizations from Discover.

Module 3 — Visualizing data

• Lens editor: workspace, fields list, layer pane; types Tabular, Bar, Goal, Line/Area, Magnitude, Map, Proportion; multi-layers and quick functions.
• Adjusting visualizations: visual options, legends, axes, time shift, series colors, value formatting.
• Creating maps: Maps editor, layer management (fit to data, show/hide, edit, clone), stacking layers.
• Labs: build Lens visualizations, adjust visualizations and create maps.

Module 4 — Complementary visualizations

• Text and metrics: Markdown panels (links, images, navigation), Metric visualization (primary, secondary, break down by, supporting visualization).
• Tables: rows, split metrics, metrics, conditional coloring of values.
• Interactive dashboards: click-to-filter, add filter, drilldowns to another dashboard or a parameterized URL.
• Labs: Metrics and Markdown, tables and interactive dashboards.

Module 5 — Presenting data

• Sharing a dashboard: custom branding, direct links (saved object vs snapshot), embed / iframe, anonymous authentication, PDF/PNG reports.
• Sharing with users: Kibana RBAC, Elasticsearch roles (cluster / index privileges, field/document-level access), Kibana roles.
• Canvas: workpads and templates, static elements and visualizations, data sources, expressions, infographic PDF export.
• Labs: sharing links and PDF, viewer role and user, Canvas presentation.

Module 6 — Analyzing data with Machine Learning

• Introduction to Elastic ML: supervised / unsupervised, detectors, functions, influencers, job wizard (single / multi-metric, population, rare…), bucket span, forecasting.
• Analyzing results: Anomaly Explorer and Single Metric Viewer, bucket / record score, influencers, annotations.
• Data Frame Analytics: transforms (Pivot, Latest), outlier detection.
• AIOps Labs: explain log rate spikes, log pattern analysis, change point detection.
• Labs: single- and multi-metric jobs, AIOps Labs.

Module 7 — Advanced Kibana

• Lens formulas: filter ratio, week-over-week, percent of total; time series functions (cumulative_sum, moving_average), examples with shift='1w'.
• Runtime fields: schema on write vs schema on read, Painless (emit), creation from Data Views, Discover or Lens, benefits and trade-offs.
• Vega: custom visualizations in Vega / Vega-Lite, data sources, data clause, dynamic tokens (%timefield%, %context%, %timefilter%).
• Labs: runtime fields and Vega visualizations.

Module 8 — Alerting

• Rules and connectors: anatomy of a rule (schedule, conditions, actions), built-in connectors (Email, Index, Slack, PagerDuty, ServiceNow, Webhook…).
• In-app alerts: Discover (Elasticsearch query), Maps (tracking containment), ML (anomaly detection, Bucket / Record / Influencer types, severity).
• Managing alerts: Stack Management > Rules, statuses, history, snooze, maintenance windows, troubleshooting.
• Lab: create in-app alerts.

Mathieu ELIE is your instructor. With over 110 training sessions delivered on Elastic technologies, your instructor also spends 50% of his time in production as an elk and elastic stack consultant . So you get an instructor who also has real production experience.

Learn more about the instructor .