112 lines
4.2 KiB
Plaintext
112 lines
4.2 KiB
Plaintext
[[connect-to-elasticsearch]]
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== Add data to {kib}
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To start working with your data in {kib}, you can:
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* Upload a CSV, JSON, or log file with the File Data Visualizer.
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* Upload geospatial data with the GeoJSON Upload feature.
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* Index logs, metrics, events, or application data by setting up a Beats module.
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* Connect {kib} with existing {es} indices.
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If you're not ready to use your own data, you can add a <<get-data-in, sample data set>>
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to see all that you can do in {kib}.
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[float]
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[[upload-data-kibana]]
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=== Upload a CSV, JSON, or log file
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experimental[]
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To visualize data in a CSV, JSON, or log file, you can upload it using the File
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Data Visualizer. On the home page, click *Import a CSV, NDSON, or log file*, and
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then drag your file into the File Data Visualizer. Alternatively, you can open
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it by navigating to the Machine Learning app page from the sidebar menu and
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selecting the Data Visualizer from the top navigation bar on the opening page.
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[role="screenshot"]
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image::images/data-viz-homepage.jpg[File Data Visualizer on the home page]
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You can upload a file up to 100 MB. This value is configurable up to 1 GB in
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<<kibana-ml-settings, Advanced Settings>>.
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[role="screenshot"]
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image::images/add-data-fv.png[File Data Visualizer]
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The File Data Visualizer uses the {ref}/ml-find-file-structure.html[find_file_structure API] to analyze
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the uploaded file and to suggest ingest pipelines and mappings for your data.
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NOTE: This feature is not intended for use as part of a
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repeated production process, but rather for the initial exploration of your data.
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[float]
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[[upload-geoipdata-kibana]]
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=== Upload geospatial data
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To visualize geospatial data in a point or shape file, you can upload it using the <<geojson-upload, GeoJSON Upload>>
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feature in Maps, and then use that data as a layer in a map.
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The data is also available for use in the broader Kibana ecosystem, for example,
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in visualizations and Canvas workpads.
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With GeoJSON Upload, you can upload a file up to 50 MB.
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[role="screenshot"]
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image::images/fu_gs_select_source_file_upload.png[]
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[float]
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[[add-data-tutorial-kibana]]
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=== Index metrics, log, security, and application data
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The built-in data tutorials can help you quickly get up and running with
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metrics data, log analytics, security events, and application data.
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These tutorials walk you through installing and configuring a
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Beats data shipper to periodically collect and send data to {es}.
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You can then use the pre-built dashboards to explore and analyze the data.
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You access the tutorials from the home page.
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If a tutorial doesn’t exist for your data, go to the {beats-ref}/beats-reference.html[Beats overview]
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to learn about other data shippers in the Beats family.
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[role="screenshot"]
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image::images/add-data-tutorials.png[Add Data tutorials]
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[float]
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[[connect-to-es]]
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=== Connect with {es} indices
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To visualize data in existing {es} indices, you must
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create an index pattern that matches the names of the indices that you want to explore.
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When you add data with the File Data Visualizer, GeoJSON Upload feature,
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or built-in tutorial, an index pattern is created for you.
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. Go to *Stack Management*, and then click *Index Patterns*.
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. Click *Create index pattern*.
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. Specify an index pattern that matches the name of one or more of your Elasticsearch indices.
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+
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For example, an index pattern can point to your Apache data from yesterday,
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`filebeat-apache-4-3-2022`, or any index that matches the pattern, `filebeat-*`.
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Using a wildcard is the more popular approach.
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. Click *Next Step*, and then select the index field that contains the timestamp you want to use to perform time-based
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comparisons.
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+
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Kibana reads the index mapping and lists all fields that contain a timestamp. If your
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index doesn't have time-based data, choose *I don't want to use the Time Filter*.
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+
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You must select a time field to use global time filters on your dashboards.
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. Click *Create index pattern*.
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+
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{kib} is now configured to access your {es} indices.
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You’ll see a list of fields configured for the matching index.
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You can designate your index pattern as the default by clicking the star icon on this page.
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+
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When searching in *Discover* and creating visualizations, you choose a pattern
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from the index pattern menu to specify the {es} indices that contain the data you want to explore.
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