[[connect-to-elasticsearch]]
== Add data to {kib}

To start working with your data in {kib}, you can:

* Upload a CSV, JSON, or log file with the File Data Visualizer.

* Upload geospatial data with the GeoJSON Upload feature.

* Index logs, metrics, events, or application data by setting up a Beats module.

* Connect {kib} with existing {es} indices.

If you're not ready to use your own data, you can add a <<get-data-in, sample data set>>
to see all that you can do in {kib}.

[float]
[[upload-data-kibana]]
=== Upload a CSV, JSON, or log file

experimental[]

To visualize data in a CSV, JSON, or log file, you can upload it using the File 
Data Visualizer. On the home page, click *Import a CSV, NDSON, or log file*, and 
then drag your file into the File Data Visualizer. Alternatively, you can open 
it by navigating to *Machine Learning* from the side navigation and selecting 
*Data Visualizer*.

[role="screenshot"]
image::images/data-viz-homepage.jpg[File Data Visualizer on the home page]

You can upload a file up to 100 MB. This value is configurable up to 1 GB in
<<kibana-ml-settings, Advanced Settings>>.

[role="screenshot"]
image::images/add-data-fv.png[File Data Visualizer]

The File Data Visualizer uses the {ref}/ml-find-file-structure.html[find_file_structure API] to analyze
the uploaded file and to suggest ingest pipelines and mappings for your data.

NOTE: This feature is not intended for use as part of a
repeated production process, but rather for the initial exploration of your data.

[float]
[[upload-geoipdata-kibana]]
=== Upload geospatial data

To visualize geospatial data in a point or shape file, you can upload it using the <<geojson-upload, GeoJSON Upload>>
feature in Maps, and then use that data as a layer in a map.
The data is also available for use in the broader Kibana ecosystem, for example,
in visualizations and Canvas workpads.
With GeoJSON Upload, you can upload a file up to 50 MB.

[role="screenshot"]
image::images/fu_gs_select_source_file_upload.png[]


[float]
[[add-data-tutorial-kibana]]
===  Index metrics, log, security, and application data

The built-in data tutorials can help you quickly get up and running with
metrics data, log analytics, security events, and application data.
These tutorials walk you through installing and configuring a
Beats data shipper to periodically collect and send data to {es}.
You can then use the pre-built dashboards to explore and analyze the data.

You access the tutorials from the home page.
If a tutorial doesn’t exist for your data, go to the {beats-ref}/beats-reference.html[Beats overview]
to learn about other data shippers in the Beats family.

[role="screenshot"]
image::images/add-data-tutorials.png[Add Data tutorials]


[float]
[[connect-to-es]]
=== Connect with {es} indices

To visualize data in existing {es} indices, you must
create an index pattern that matches the names of the indices that you want to explore.
When you add data with the File Data Visualizer, GeoJSON Upload feature,
or built-in tutorial, an index pattern is created for you.

. Go to *Stack Management*, and then click *Index Patterns*.

. Click *Create index pattern*.

. Specify an index pattern that matches the name of one or more of your Elasticsearch indices.
+
For example, an index pattern can point to your Apache data from yesterday,
`filebeat-apache-4-3-2022`, or any index that matches the pattern, `filebeat-*`.
Using a wildcard is the more popular approach.


. Click *Next Step*, and then select the index field that contains the timestamp you want to use to perform time-based
comparisons.
+
Kibana reads the index mapping and lists all fields that contain a timestamp. If your
index doesn't have time-based data, choose *I don't want to use the Time Filter*.
+
You must select a time field to use global time filters on your dashboards.

. Click *Create index pattern*.
+
{kib} is now configured to access your {es} indices.
You’ll see a list of fields configured for the matching index.
You can designate your index pattern as the default by clicking the star icon on this page.
+
When searching in *Discover* and creating visualizations, you choose a pattern
from the index pattern menu to specify the {es} indices that contain the data you want to explore.
