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## Summary Related to https://github.com/elastic/kibana/issues/161040 This PR deletes a line from the quick start guide to make it up-to-date.
153 lines
6 KiB
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153 lines
6 KiB
Text
[[get-started]]
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== Quick start
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To quickly get up and running with {kib}, set up on Cloud, then add a sample data set that you can explore and visualize.
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When you're done, you'll know how to:
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* <<explore-the-data,Explore the data with *Discover*.>>
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* <<view-and-analyze-the-data,Visualize the data with *Dashboard*.>>
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[float]
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=== Required privileges
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You must have `read`, `write`, and `manage` privileges on the `kibana_sample_data_*` indices.
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Learn how to <<tutorial-secure-access-to-kibana, secure access to {kib}>>, or refer to {ref}/security-privileges.html[Security privileges] for more information.
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[float]
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[[set-up-on-cloud]]
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== Set up on cloud
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include::{docs-root}/shared/cloud/ess-getting-started.asciidoc[]
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[float]
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[[gs-get-data-into-kibana]]
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== Add the sample data
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Sample data sets come with sample visualizations, dashboards, and more to help you explore {kib} before you ingest or add your own data.
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. On the home page, click *Try sample data*.
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. Click *Other sample data sets*.
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. On the *Sample eCommerce orders* card, click *Add data*.
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+
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[role="screenshot"]
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image::images/addData_sampleDataCards_8.6.0.png[Add data UI for the sample data sets]
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[float]
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[[explore-the-data]]
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== Explore the data
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*Discover* displays the data in an interactive histogram that shows the distribution of data, or documents, over time, and a table that lists the fields for each document that matches the {data-source}. To view a subset of the documents, you can apply filters to the data, and customize the table to display only the fields you want to explore.
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. Open the main menu, then click *Discover*.
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. Change the <<set-time-filter, time filter>> to *Last 7 days*.
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+
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[role="screenshot"]
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image::images/timeFilter_discover_8.4.0.png[Time filter menu with Last 7 days filter configured]
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. To view the sales orders for women's clothing that are $60 or more, use the <<kuery-query,*KQL*>> search field:
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+
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[source,text]
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products.taxless_price >= 60 and category : Women's Clothing
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[role="screenshot"]
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image::images/kql_discover_8.4.0.png[Discover tables that displays only the orders for women's clothing that are $60 or more]
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. To view only the product categories that contain sales orders, hover over the *category* field, then click *+*.
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+
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[role="screenshot"]
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image::images/availableFields_discover_8.4.0.png[Discover table that displays only the product categories that contain orders]
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[float]
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[[view-and-analyze-the-data]]
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== Visualize the data
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A dashboard is a collection of panels that you can use to visualize the data. Panels contain visualizations, interactive controls, text, and more.
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. Open the main menu, then click *Dashboard*.
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. Click *[eCommerce] Revenue Dashboard*.
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+
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[role="screenshot"]
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image::images/dashboard_ecommerceRevenueDashboard_8.6.0.png[The [eCommerce] Revenue Dashboard that comes with the Sample eCommerce order data set]
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[float]
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[[create-a-visualization]]
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=== Create a visualization panel
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Create a treemap visualization panel that shows the top sales regions and manufacturers, then add the panel to the dashboard.
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. In the toolbar, click *Edit*.
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. On the dashboard, click *Create visualization*.
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. In the drag-and-drop visualization editor, open the *Visualization type* dropdown, then select *Treemap*.
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+
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[role="screenshot"]
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image::getting-started/images/visualizationTypeDropdown_lens_8.4.0.png[Chart type menu with Treemap selected]
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. From the *Available fields* list, drag the following fields to the workspace:
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* *geoip.city_name*
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* *manufacturer.keyword*
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[role="screenshot"]
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image::getting-started/images/ecommerceTreemap_lens_8.4.0.png[Treemap that displays Top values of geoip.city_name and Top values or manufacturer.keyword fields]
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. Click *Save and return*.
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+
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The treemap appears as the last visualization panel on the dashboard.
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[float]
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[[interact-with-the-data]]
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=== Interact with the data
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You can interact with the dashboard data using controls that allow you to apply dashboard-level filters. Interact with the *[eCommerce] Controls* panel to view the women's clothing data from the Gnomehouse manufacturer.
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. From the *Manufacturer* dropdown, select *Gnomehouse*.
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. From the *Category* dropdown, select *Women's Clothing*.
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+
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[role="screenshot"]
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image::images/sampleDataFilter_dashboard_8.6.0.png[The [eCommerce] Revenue Dashboard that shows only the women's clothing data from the Gnomehouse manufacturer]
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[float]
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[[filter-and-query-the-data]]
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=== Filter the data
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To view a subset of the data, you can apply filters to the dashboard data. Apply a filter to view the women's clothing data generated on Wednesday from the Gnomehouse manufacturer.
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. Click *Add filter*.
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[role="screenshot"]
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image::images/addFilter_dashboard_8.6.0.png[The Add filter action that applies dashboard-level filters]
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. From the *Field* dropdown, select *day_of_week*.
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. From the *Operator* dropdown, select *is*.
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. From the *Value* dropdown, select *Wednesday*.
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+
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[role="screenshot"]
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image::images/addFilterOptions_dashboard_8.6.0.png[The Add filter options configured to display only the women's clothing data generated on Wednesday from the Gnomehouse manufacturer]
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. Click *Add filter*.
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+
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[role="screenshot"]
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image::images/dashboard_sampleDataAddFilter_8.6.0.png[The [eCommerce] Revenue Dashboard that shows only the women's clothing data generated on Wednesday from the Gnomehouse manufacturer]
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[float]
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[[quick-start-whats-next]]
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== What's next?
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*Add your own data.* Ready to add your own data? Go to {observability-guide}/observability-get-started.html[Get started with Elastic Observability], or go to <<connect-to-elasticsearch,Add data to {kib}>> and learn about all the ways you can add data.
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*Explore your own data in Discover.* Ready to learn more about exploring your data in *Discover*? Go to <<discover, Discover>>.
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*Create a dashboard with your own data.* Ready to learn more about visualizing your data on a *Dashboard*? Go to <<dashboard, Dashboard>>.
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*Try out the {ml-features}.* Ready to analyze the sample data sets and generate models for its patterns of behavior? Go to {ml-docs}/ml-getting-started.html[Getting started with {ml}].
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