kibana/docs/getting-started/tutorial-full-experience.asciidoc

[[create-your-own-dashboard]]
== Create your own dashboard

Ready to add data to {kib} and create your own dashboard? In this tutorial, you'll use three types of data sets that'll help you learn to:

* <<load-the-data-sets, Load data into Elasticsearch>>
* <<tutorial-define-index, Define an index pattern>>
* <<explore-your-data, Discover and explore data>>
* <<tutorial-visualizing, Visualize data>>

[float]
[[download-the-data]]
=== Download the data

To complete the tutorial, you'll download and use the following data sets:

* The complete works of William Shakespeare, suitably parsed into fields
* A set of fictitious bank accounts with randomly generated data
* A set of randomly generated log files

Create a new working directory where you want to download the files. From that directory, run the following commands:

[source,shell]
curl -O https://download.elastic.co/demos/kibana/gettingstarted/8.x/shakespeare.json
curl -O https://download.elastic.co/demos/kibana/gettingstarted/8.x/accounts.zip
curl -O https://download.elastic.co/demos/kibana/gettingstarted/8.x/logs.jsonl.gz

Two of the data sets are compressed. To extract the files, use the following commands:

[source,shell]
unzip accounts.zip
gunzip logs.jsonl.gz

[float]
==== Structure of the data sets

The Shakespeare data set has the following structure:

[source,json]
{
    "line_id": INT,
    "play_name": "String",
    "speech_number": INT,
    "line_number": "String",
    "speaker": "String",
    "text_entry": "String",
}

The accounts data set has the following structure:

[source,json]
{
    "account_number": INT,
    "balance": INT,
    "firstname": "String",
    "lastname": "String",
    "age": INT,
    "gender": "M or F",
    "address": "String",
    "employer": "String",
    "email": "String",
    "city": "String",
    "state": "String"
}

The logs data set has dozens of different fields. The notable fields include the following:

[source,json]
{
    "memory": INT,
    "geo.coordinates": "geo_point"
    "@timestamp": "date"
}

[float]
==== Set up mappings

Before you load the Shakespeare and logs data sets, you must set up {ref}/mapping.html[_mappings_] for the fields.
Mappings divide the documents in the index into logical groups and specify the characteristics
of the fields. These characteristics include the searchability of the field
and whether it's _tokenized_, or broken up into separate words.

NOTE: If security is enabled, you must have the `all` Kibana privilege to run this tutorial.
You must also have the `create`, `manage` `read`, `write,` and `delete` 
index privileges. See {ref}/security-privileges.html[Security privileges] 
for more information.

Open the menu, then go to *Dev Tools*. On the *Console* page, set up a mapping for the Shakespeare data set:

[source,js]
PUT /shakespeare
{
  "mappings": {
    "properties": {
    "speaker": {"type": "keyword"},
    "play_name": {"type": "keyword"},
    "line_id": {"type": "integer"},
    "speech_number": {"type": "integer"}
    }
  }
}

//CONSOLE

The mapping specifies field characteristics for the data set:

* The `speaker` and `play_name` fields are keyword fields. These fields are not analyzed.
The strings are treated as a single unit even if they contain multiple words.

* The `line_id` and `speech_number` fields are integers.

The logs data set requires a mapping to label the latitude and longitude pairs
as geographic locations by applying the `geo_point` type.

[source,js]
PUT /logstash-2015.05.18
{
  "mappings": {
    "properties": {
      "geo": {
        "properties": {
          "coordinates": {
            "type": "geo_point"
          }
        }
      }
    }
  }
}

//CONSOLE

[source,js]
PUT /logstash-2015.05.19
{
  "mappings": {
    "properties": {
      "geo": {
        "properties": {
          "coordinates": {
            "type": "geo_point"
          }
        }
      }
    }
  }
}

//CONSOLE

[source,js]
PUT /logstash-2015.05.20
{
  "mappings": {
    "properties": {
      "geo": {
        "properties": {
          "coordinates": {
            "type": "geo_point"
          }
        }
      }
    }
  }
}

//CONSOLE

The accounts data set doesn't require any mappings.

[float]
[[load-the-data-sets]]
==== Load the data sets

At this point, you're ready to use the Elasticsearch {ref}/docs-bulk.html[bulk]
API to load the data sets:

[source,shell]
curl -u elastic -H 'Content-Type: application/x-ndjson' -XPOST '<host>:<port>/bank/_bulk?pretty' --data-binary @accounts.json
curl -u elastic -H 'Content-Type: application/x-ndjson' -XPOST '<host>:<port>/shakespeare/_bulk?pretty' --data-binary @shakespeare.json
curl -u elastic -H 'Content-Type: application/x-ndjson' -XPOST '<host>:<port>/_bulk?pretty' --data-binary @logs.jsonl

Or for Windows users, in Powershell:
[source,shell]
Invoke-RestMethod "http://<host>:<port>/bank/account/_bulk?pretty" -Method Post -ContentType 'application/x-ndjson' -InFile "accounts.json"
Invoke-RestMethod "http://<host>:<port>/shakespeare/_bulk?pretty" -Method Post -ContentType 'application/x-ndjson' -InFile "shakespeare.json"
Invoke-RestMethod "http://<host>:<port>/_bulk?pretty" -Method Post -ContentType 'application/x-ndjson' -InFile "logs.jsonl"

These commands might take some time to execute, depending on the available computing resources.

When you define an index pattern, the indices that match the pattern must
exist in {es} and contain data. 

To verify the availability of the indices, open the menu, go to *Dev Tools > Console*, then enter:

[source,js]
GET /_cat/indices?v

Alternately, use:

[source,shell]
`curl -XGET "http://localhost:9200/_cat/indices"`.

The output should look similar to:

[source,shell]
health status index               pri rep docs.count docs.deleted store.size pri.store.size
yellow open   bank                  1   1       1000            0    418.2kb        418.2kb
yellow open   shakespeare           1   1     111396            0     17.6mb         17.6mb
yellow open   logstash-2015.05.18   1   1       4631            0     15.6mb         15.6mb
yellow open   logstash-2015.05.19   1   1       4624            0     15.7mb         15.7mb
yellow open   logstash-2015.05.20   1   1       4750            0     16.4mb         16.4mb