[[tutorial-build-dashboard]]
== Build your own dashboard

Want to load some data into Kibana and build a dashboard? This tutorial shows you how to:

* <<tutorial-load-dataset, Load a data set into Elasticsearch>>
* <<tutorial-define-index, Define an index pattern to connect to Elasticsearch>>
* <<tutorial-discovering, Discover and explore the data>>
* <<tutorial-visualizing, Visualize the data>>
* <<tutorial-dashboard, Add visualizations to a dashboard>>

When you complete this tutorial, you'll have a dashboard that looks like this.

[role="screenshot"]
image::images/tutorial-dashboard.png[]

[float]
[[tutorial-load-dataset]]
=== Load sample data

This tutorial requires you to download three data sets:

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

[float]
==== Download the data sets

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 these commands:

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

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

The Shakespeare data set has this structure:

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

The accounts data set is structured as follows:

[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. Here are the notable fields for this tutorial:

[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.

In Kibana *Dev Tools > Console*, 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

This 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

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.

Verify successful loading:

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

//CONSOLE

Your output should look similar to this:

[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