Complete Guide to Data Visualization with Kibana 8

Explore Elasticsearch terms and structure, including documents, indexes, mappings, dynamic mapping, and templates, and learn how shards, replicas, nodes, and clusters enable scalable search and data modeling.

Learn to use Kibana dev tools to access the Elasticsearch API, view cluster health, shards, nodes, indices, and shard allocation with common get and cat commands.

Add sample data in kibana by selecting try sample data and the sample flights set to populate data views, a dashboard, and an index pattern, then explore Discover for documents.

Add sample data from CSV files by selecting and importing the Titanic CSV, creating an index, and viewing it in Discover to see all documents.

Explore how to inspect a flight data schema in Elasticsearch and Kibana 8 by examining field types, mapping, and geolocation, including average ticket price, delays, carrier, and origin–destination details.

Explore the discover feature in Kibana 8 by examining flight logs, viewing document statistics, applying filters and queries, and exporting logs to CSV.

Explore how to see logs by time in Kibana's discover tab, using relative and absolute date filters, updating queries, and dragging the time range to filter documents and view counts.

Explore Kibana 8 field statistics, analyzing numeric meaning like mean and median, boolean values, and geolocation distributions to understand ticket prices and delays.

Export Kibana data to csv by selecting day zero and a carrier. Include fields like carrier, average ticket price, is canceled, day of week, and country, then download to excel.

Save a Kibana query labeled day of week zero Kibana Airlines, then open and apply a day-of-week filter to view results for that selection.

Explore Kibana 8 basics by examining visualization types such as pie, area, and metric, then advance to data table and heatmap, and learn to choose the graph for your data.

Create an area visualization in Kibana using a date histogram on the x axis to show document counts over time from the flights data, split by carriers with stack.

Create a data table visualization in Kibana using the Kibana sample data flights, split by day of week and origin city, separate canceled flights, and save to the library.

Build a Kibana heatmap by mapping hour of day to the x axis and city to the y axis, visualizing document counts with color intensity, and saving the visualization.

Create a Kibana 8 line visualization of US data using a date histogram x-axis, split by cancellation status and flight delay, and customize metrics, axes, and legend.

Explore metric visualizations in Kibana, which display a single number by combining counts and averages across groups, such as canceled versus not canceled flights and their average ticket price.

Build a horizontal bar chart in Kibana using aggregation base, a time x-axis with a three-hour interval, and stack by desk country to view per-hour counts.

Learn to build a Kibana 8 pie visualization, split slices by day of week and country, customize legends, donuts, values, and percentages, and save your visualization.

Create a vertical bar visualization in Kibana 8 using the flights sample data, with a timestamp date histogram and splits by hour of the day.

Learn how to create Kibana visualizations with lens using drag-and-drop, switch visualization types, and explore metrics like region, day of week, and country.

Create a simple text visualization in Kibana, using the text tool with markdown headings and bold, then update and save the Kibana course dashboard.

Build a Kibana 8 filter dashboard using input controls—option list and range slider—to filter flights data by country and average ticket price, then save and add it to the dashboards.

Explore maps in Kibana 8 by building layouts and inserting documents above the maps. Create heat maps, point-to-point and polygon layouts for dashboards using flight origin data.

Explore how Kibana 8 maps use layouts to add layers, from the base map to documents and heatmaps, including GeoJSON uploads and Elasticsearch clusters.

Add documents to a Kibana map by creating a layer and choosing a data view, then visualize airports with color by country and size by delay minutes.

Create a cluster map in Kibana using sample data flights; switch from count to sum of delays, size by delay, and color by average ticket price to reveal regional patterns.

Learn how to combine visualizations and maps into a single dashboard, add text, controls, and graphs, apply filters, and include discover logs to build your own Kibana dashboards.

Add text and controls from the library in Kibana, customize the title, and adjust its position. Select the city and the average ticket price to populate the visuals.

Add graphs in Kibana 8 by selecting from library, including maps, data table, and flight log. Save your layout to display cluster heatmap, line, pie, and vertical bar visuals.

Learn to filter Kibana 8 visualizations by applying range filters on the map (price 100-500) and timestamp hours, then propagate changes across discover, dashboards, and individual visuals.

Export data from Kibana dashboards by opening the settings, selecting more, and using inspect to download csv files from tables and charts.

Explore Kibana stack management: view and manage indices, apply index lifecycle policies with hot, warm, and cold phases, and configure data views, saved objects, tags, spaces, and display options.

Learn how Kibana spaces isolate and organize data by creating team-specific environments with restricted access. Discover how to create, manage, and copy dashboards across spaces using stack management.

Recap the Kibana 8 course with Elasticsearch setup, cluster, shard, and node concepts, and creating visualizations, maps, and a dashboard, plus discover, filtering, and Kibana management settings.