Updates getting started guide (#41778)

docs/canvas/canvas-getting-started.asciidoc

@@ -7,7 +7,7 @@ To get up and running with Canvas, use the following tutorial where you'll creat
 [float]
 === Before you begin
 
-For this tutorial, you'll need to add the {kibana-ref}/add-sample-data.html[Sample eCommerce orders data].
+For this tutorial, you'll need to add the <<add-sample-data, Sample eCommerce orders data>>.
 
 [float]
 === Create and personalize your workpad

docs/canvas/canvas-workpad.asciidoc

@@ -14,7 +14,7 @@ When you create a workpad, you'll start with a blank page, or you can choose a w
 
 * To import an existing workpad, click and drag a workpad JSON file to the *Import workpad JSON file* field.
 
-For advanced workpad examples, add a {kibana-ref}/add-sample-data.html[sample Kibana data set], then select *Canvas* from the *View Data* dropdown list.
+For advanced workpad examples, add a <<add-sample-data, sample Kibana data set>>, then select *Canvas* from the *View Data* dropdown list.
 
 For more workpad inspiration, go to the link:https://www.elastic.co/blog/[Elastic Blog]. 
 

docs/getting-started.asciidoc

@@ -4,47 +4,57 @@
 [partintro]
 --
 
-Ready to get some hands-on experience with {kib}? There are two ways to start:
+You’re new to Kibana and want to give it a try. {kib} has sample data sets and 
+tutorials to help you get started.
 
-* <<tutorial-sample-data, Explore {kib} using the Flights dashboard>>
-+
-Load the Flights sample data and dashboard with one click and start 
-interacting with {kib} visualizations in seconds.
+[float]
+=== Sample data
 
-* <<tutorial-build-dashboard, Build your own dashboard>> 
-+
-Manually load a data set and build your own visualizations and dashboard.
+You can use the <<add-sample-data, sample data 
+sets>> to take {kib} for a test ride without having 
+to go through the process of loading data yourself. With one click, 
+you can install a sample data set and start interacting with 
+{kib} visualizations in seconds. You can access the sample data 
+from the {kib} home page.
 
-Before you begin, make sure you've <<install, installed Kibana>> and established
-a {kibana-ref}/connect-to-elasticsearch.html[connection to Elasticsearch].
-You might also be interested in the
-https://www.elastic.co/webinars/getting-started-kibana[Getting Started with Kibana]
-video tutorial.
+[float]
+
+=== Add data tutorials
+{kib} has built-in *Add Data* tutorials to help you set up
+data flows in the Elastic Stack. These tutorials are available 
+from the Kibana home page. In *Add Data to Kibana*, find the data type 
+you’re interested in, and click its button to view a list of available tutorials. 
+
+[float]
+=== Hands-on experience 
+
+The following tutorials walk you through searching, analyzing, 
+and visualizing data.
+
+* <<tutorial-sample-data, Explore Kibana using sample data>>. You'll
+learn to filter and query data, edit visualizations, and interact with dashboards. 
+
+* <<tutorial-build-dashboard, Build your own dashboard>>.  You'll manually load a data set and build 
+your own visualizations and dashboard. 
+
+[float]
+=== Before you begin
+
+Make sure you've <<install, installed Kibana>> and established
+a <<connect-to-elasticsearch, connection to Elasticsearch>>.
 
 If you are running our https://cloud.elastic.co[hosted Elasticsearch Service] 
 on Elastic Cloud, you can access Kibana with a single click.
+
+
 --
 
 include::getting-started/add-sample-data.asciidoc[]
 
 include::getting-started/tutorial-sample-data.asciidoc[]
 
-include::getting-started/tutorial-sample-filter.asciidoc[]
-
-include::getting-started/tutorial-sample-query.asciidoc[]
-
-include::getting-started/tutorial-sample-discover.asciidoc[]
-
-include::getting-started/tutorial-sample-edit.asciidoc[]
-
-include::getting-started/tutorial-sample-inspect.asciidoc[]
-
-include::getting-started/tutorial-sample-remove.asciidoc[]
-
 include::getting-started/tutorial-full-experience.asciidoc[]
 
-include::getting-started/tutorial-load-dataset.asciidoc[]
-
 include::getting-started/tutorial-define-index.asciidoc[]
 
 include::getting-started/tutorial-discovering.asciidoc[]
@@ -53,6 +63,3 @@ include::getting-started/tutorial-visualizing.asciidoc[]
 
 include::getting-started/tutorial-dashboard.asciidoc[]
 
-include::getting-started/tutorial-inspect.asciidoc[]
-
-include::getting-started/wrapping-up.asciidoc[]

docs/getting-started/add-sample-data.asciidoc

@@ -1,32 +1,28 @@
 [[add-sample-data]]
-== Get up and running with sample data
+== Add sample data
 
 {kib} has several sample data sets that you can use to explore {kib} before loading your own data.
-Sample data sets install prepackaged visualizations, dashboards,
-{kibana-ref}/canvas-getting-started.html[Canvas workpads],
-and {kibana-ref}/maps.html[Maps].
-
-The sample data sets showcase a variety of use cases:
+These sample data sets showcase a variety of use cases:
 
 * *eCommerce orders* includes visualizations for product-related information,
 such as cost, revenue, and price.
+* *Flight data* enables you to view and interact with flight routes.
 * *Web logs* lets you analyze website traffic.
-* *Flight data* enables you to view and interact with flight routes for four airlines.
 
-To get started, go to the home page and click the link next to *Add sample data*.
-
-Once you have loaded a data set, click *View data* to view visualizations in *Dashboard*.
-
-*Note:* The timestamps in the sample data sets are relative to when they are installed.
-If you uninstall and reinstall a data set, the timestamps will change to reflect the most recent installation.
+To get started, go to the {kib} home page and click the link underneath *Add sample data*.
 
+Once you've loaded a data set, click *View data* to view prepackaged 
+visualizations, dashboards, Canvas workpads, Maps, and Machine Learning jobs.
 
 [role="screenshot"]
 image::images/add-sample-data.png[]
 
+NOTE: The timestamps in the sample data sets are relative to when they are installed.
+If you uninstall and reinstall a data set, the timestamps will change to reflect the most recent installation.
+
 [float]
-==== Next steps
+=== Next steps
 
-Play with the sample flight data in the {kibana-ref}/tutorial-sample-data.html[flight dashboard tutorial].
+* Explore {kib} by following the <<tutorial-sample-data, sample data tutorial>>.
 
-Learn how to load data, define index patterns and build visualizations by {kibana-ref}/tutorial-build-dashboard.html[building your own dashboard].
+* Learn how to load data, define index patterns, and build visualizations by <<tutorial-build-dashboard, building your own dashboard>>.

docs/getting-started/tutorial-dashboard.asciidoc

@@ -1,27 +1,57 @@
 [[tutorial-dashboard]]
-=== Displaying your visualizations in a dashboard
+=== Add visualizations to a dashboard
 
 A dashboard is a collection of visualizations that you can arrange and share.
 You'll build a dashboard that contains the visualizations you saved during
 this tutorial.
 
 . Open *Dashboard*.
-. Click *Create new dashboard*.
-. Click *Add*.
+. On the Dashboard overview page, click *Create new dashboard*.
+. Click *Add* in the menu bar.
 . Add *Bar Example*, *Map Example*, *Markdown Example*, and *Pie Example*.
-
-
-Your sample dashboard look like this:
-
++
+Your sample dashboard should look like this:
++
 [role="screenshot"]
 image::images/tutorial-dashboard.png[]
 
+. Try out the editing controls.
++
 You can rearrange the visualizations by clicking a the header of a
 visualization and dragging. The gear icon in the top right of a visualization
 displays controls for editing and deleting the visualization.  A resize control
 is on the lower right.
 
-To get a link to share or HTML code to embed the dashboard in a web page, save
-the dashboard and click  *Share*.
+. *Save* your dashboard.
+
+==== Inspect the data
+
+Seeing visualizations of your data is great, 
+but sometimes you need to look at the actual data to
+understand what's really going on. You can inspect the data behind any visualization
+and view the {es} query used to retrieve it.
+
+. In the dashboard, hover the pointer over the pie chart, and then click the icon in the upper right. 
+. From the *Options* menu, select *Inspect*.
++
+[role="screenshot"]
+image::images/tutorial-full-inspect1.png[]
+
+. To look at the query used to fetch the data for the visualization, select *View > Requests*
+in the upper right of the Inspect pane.
+
+[float]
+=== Next steps
+
+Now that you have a handle on the basics, you're ready to start exploring
+your own data with Kibana.
+
+* See {kibana-ref}/discover.html[Discover] for information about searching and filtering
+your data.
+* See {kibana-ref}/visualize.html[Visualize] for information about the visualization
+types Kibana has to offer.
+* See {kibana-ref}/management.html[Management] for information about configuring Kibana
+and managing your saved objects.
+* See {kibana-ref}/console-kibana.html[Console] to learn about the interactive
+console you can use to submit REST requests to Elasticsearch.
 
-*Save* your dashboard.

docs/getting-started/tutorial-define-index.asciidoc

@@ -1,45 +1,53 @@
 [[tutorial-define-index]]
-=== Defining your index patterns
+=== Define your index patterns
 
 Index patterns tell Kibana which Elasticsearch indices you want to explore.
 An index pattern can match the name of a single index, or include a wildcard
-(*) to match multiple indices.
+(*) to match multiple indices. 
 
 For example, Logstash typically creates a
 series of indices in the format `logstash-YYYY.MMM.DD`. To explore all
 of the log data from May 2018, you could specify the index pattern
 `logstash-2018.05*`.
 
-You'll create patterns for the Shakespeare data set, which has an
+
+[float]
+==== Create your first index pattern
+
+First you'll create index patterns for the Shakespeare data set, which has an
 index named `shakespeare,` and the accounts data set, which has an index named
-`bank.` These data sets don't contain time-series data.
+`bank`. These data sets don't contain time series data.
 
 . In Kibana, open *Management*, and then click *Index Patterns.*
 . If this is your first index pattern, the *Create index pattern* page opens automatically.
-Otherwise, click *Create index pattern* in the upper left.
+Otherwise, click *Create index pattern*.
 . Enter `shakes*` in the *Index pattern* field.
 +
 [role="screenshot"]
 image::images/tutorial-pattern-1.png[]
 
 . Click *Next step*.
-. In *Configure settings*, click *Create index pattern*.  For this pattern,
-you don't need to configure any settings.
-. Define a second index pattern named  `ba*` You don't need to configure any settings for this pattern.
+. In *Configure settings*, click *Create index pattern*.
++
+You’re presented a table of all fields and associated data types in the index.
 
-Now create an index pattern for the Logstash data set. This data set
-contains time-series data.
+. Return to the *Index patterns* overview page and define a second index pattern named  `ba*`. 
+
+[float]
+==== Create an index pattern for time series data
+
+Now create an index pattern for the Logstash index, which
+contains time series data.
 
 . Define an index pattern named `logstash*`.
 . Click *Next step*.
-. In *Configure settings*, select *@timestamp* in the *Time Filter field name* dropdown menu.
+. Open the *Time Filter field name* dropdown and select *@timestamp*.
 . Click *Create index pattern*.
 
-
-
-
 NOTE: When you define an index pattern, the indices that match that pattern must
 exist in Elasticsearch and they must contain data. To check which indices are
 available, go to *Dev Tools > Console* and enter `GET _cat/indices`.  Alternately, use
 `curl -XGET "http://localhost:9200/_cat/indices"`.
 
+
+

docs/getting-started/tutorial-discovering.asciidoc

@@ -1,5 +1,5 @@
 [[tutorial-discovering]]
-=== Discovering your data
+=== Discover your data
 
 Using the Discover application, you can enter
 an {ref}/query-dsl-query-string-query.html#query-string-syntax[Elasticsearch
@@ -10,24 +10,27 @@ query] to search your data and filter the results.
 The current index pattern appears below the filter bar, in this case `shakes*`.
 You might need to click *New* in the menu bar to refresh the data.
 
-. Click the caret to the right of the current index pattern, and select `ba*`.  
+. Click the caret to the right of the current index pattern, and select `ba*`.
++
+By default, all fields are shown for each matching document. 
+
 . In the search field, enter the following string:
 +
 [source,text]
 account_number<100 AND balance>47500
-
++
 The search returns all account numbers between zero and 99 with balances in
-excess of 47,500. It returns results for account numbers 8, 32, 78, 85, and 97.
-
+excess of 47,500. Results appear for account numbers 8, 32, 78, 85, and 97.
++
 [role="screenshot"]
 image::images/tutorial-discover-2.png[]
 
-By default, all fields are shown for each matching document. To choose which
-fields to display, hover the pointer over the the list of *Available Fields*
+. To choose which
+fields to display, hover the pointer over the list of *Available fields*
 and then click *add* next to each field you want include as a column in the table.
-
++
 For example, if you add the `account_number` field, the display changes to a list of five
 account numbers.
-
++
 [role="screenshot"]
 image::images/tutorial-discover-3.png[]

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

@@ -1,12 +1,213 @@
 [[tutorial-build-dashboard]]
-== Building your own dashboard
+== Build your own dashboard
 
-Ready to load some data and build a dashboard? This tutorial shows you how to:
+Want to load some data into Kibana and build a dashboard? This tutorial shows you how to:
 
-* Load a data set into Elasticsearch
-* Define an index pattern
-* Discover and explore the data
-* Visualize the data
-* Add visualizations to a dashboard
-* Inspect the data behind a visualization
+* <<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 {xpack-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/account/_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

docs/getting-started/tutorial-inspect.asciidoc

@@ -1,24 +0,0 @@
-[[tutorial-inspect]]
-=== Inspecting the data
-
-Seeing visualizations of your data is great, 
-but sometimes you need to look at the actual data to
-understand what's really going on. You can inspect the data behind any visualization
-and view the {es} query used to retrieve it.
-
-. In the dashboard, hover the pointer over the pie chart.
-. Click the icon in the upper right. 
-. From the *Options* menu, select *Inspect*.
-+
-[role="screenshot"]
-image::images/tutorial-full-inspect1.png[]
-
-You can also look at the query used to fetch the data for the visualization. 
-
-. Open the *View:Data* menu and select *Requests*. 
-. Click the tabs to look at the request statistics, the Elasticsearch request, 
-and the response in JSON. 
-. To close the Inspector, click X in the upper right.
-+
-[role="screenshot"]
-image::images/tutorial-full-inspect2.png[]

docs/getting-started/tutorial-load-dataset.asciidoc

@@ -1,190 +0,0 @@
-[[tutorial-load-dataset]]
-=== Loading sample data
-
-This tutorial requires 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
-
-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/7.x/shakespeare.json
-curl -O https://download.elastic.co/demos/kibana/gettingstarted/7.x/accounts.zip
-curl -O https://download.elastic.co/demos/kibana/gettingstarted/7.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
-
-==== 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"
-}
-
-==== 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 {xpack-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.
-
-==== 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/account/_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

docs/getting-started/tutorial-sample-data.asciidoc

@@ -1,31 +1,207 @@
 [[tutorial-sample-data]]
-== Explore {kib} using the Flight dashboard
+== Explore {kib} using sample data
 
-You’re new to {kib} and want to try it out. With one click, you can install
-the Flights sample data and start interacting with Kibana. 
+Ready to get some hands-on experience with Kibana?
+In this tutorial, you’ll work 
+with Kibana sample data and learn to:
 
-The Flights data set contains data for four airlines.  
-You can load the data and preconfigured dashboard from the {kib} home page. 
+* <<tutorial-sample-filter, Filter and query data in visualizations>>
+* <<tutorial-sample-discover, Discover and explore your documents>>
+* <<tutorial-sample-edit, Edit a visualization>>
+* <<tutorial-sample-inspect, Inspect the data behind a visualization>>
 
-. On the home page, click the link next to  *Sample data*. 
-. On the *Sample flight data* card, click *Add*.
-. Click *View data*.
-
-You’re taken to the *Global Flight* dashboard, a collection of charts, graphs, 
-maps, and other visualizations of the the data in the `kibana_sample_data_flights` index.
-
-[role="screenshot"]
-image::images/tutorial-sample-dashboard.png[]
-
-In this tutorial, you’ll learn to:
-
-* Filter the data
-* Query the data
-* Discover the data
-* Edit a visualization
-* Inspect the data behind the scenes
 
 NOTE: If security is enabled, you must have `read`, `write`, and `manage` privileges
 on the `kibana_sample_data_*` indices. See {xpack-ref}/security-privileges.html[Security Privileges] 
 for more information.
 
+
+[float]
+=== Add sample data
+
+Install the Flights sample data set, if you haven't already.
+
+. On the {kib} home page, click the link underneath *Add sample data*. 
+. On the *Sample flight data* card, click *Add data*.
+. Once the data is added, click *View data > Dashboard*.
++
+You’re taken to the *Global Flight* dashboard, a collection of charts, graphs, 
+maps, and other visualizations of the the data in the `kibana_sample_data_flights` index.
++
+[role="screenshot"]
+image::images/tutorial-sample-dashboard.png[]
+
+[float]
+[[tutorial-sample-filter]]
+=== Filter and query the data
+
+You can use filters and queries to
+narrow the view of the data. 
+For more detailed information on these actions, see 
+{ref}/query-filter-context.html[Query and filter context].
+
+[float]
+==== Filter the data
+
+. In the *Controls* visualization, set an *Origin City* and a *Destination City*.
+. Click *Apply changes*.
++
+The `OriginCityName` and the `DestCityName` fields are filtered to match 
+the data you specified. 
++
+For example, this dashboard shows the data for flights from London to Oslo. 
++
+[role="screenshot"]
+image::images/tutorial-sample-filter.png[]
+
+. To add a filter manually, click *Add filter* in the filter bar,
+and specify the data you want to view.
+
+. When you are finished experimenting, remove all filters.
+
+
+[float]
+[[tutorial-sample-query]]
+==== Query the data
+
+. To find all flights out of Rome, enter this query in the query bar and click *Update*:
++
+[source,text]
+OriginCityName:Rome   
+
+. For a more complex query with AND and OR, try this:
++
+[source,text]
+OriginCityName:Rome AND (Carrier:JetBeats OR "Kibana Airlines")
++
+The dashboard updates to show data for the flights out of Rome on JetBeats and 
+{kib} Airlines.  
++
+[role="screenshot"]
+image::images/tutorial-sample-query.png[]
+
+. When you are finished exploring the dashboard, remove the query by 
+clearing the contents in the query bar and clicking *Update*.
+
+[float]
+[[tutorial-sample-discover]]
+=== Discover the data
+
+In Discover, you have access to every document in every index that 
+matches the selected index pattern. The index pattern tells {kib} which {es} index you are currently
+exploring. You can submit search queries, filter the 
+search results, and view document data.
+
+. In the side navigation, click *Discover*.
+
+. Ensure `kibana_sample_data_flights` is the current index pattern. 
+You might need to click *New* in the menu bar to refresh the data.
++
+You'll see a histogram that shows the distribution of 
+documents over time. A table lists the fields for 
+each matching document. By default, all fields are shown.
++
+[role="screenshot"]
+image::images/tutorial-sample-discover1.png[]
+
+. To choose which fields to display, 
+hover the pointer over the list of *Available fields*, and then click *add* next 
+to each field you want include as a column in the table.
++
+For example, if you add the `DestAirportID` and `DestWeather` fields, 
+the display includes columns for those two fields.
++
+[role="screenshot"]
+image::images/tutorial-sample-discover2.png[]
+
+[float]
+[[tutorial-sample-edit]]
+=== Edit a visualization
+
+You have edit permissions for the *Global Flight* dashboard, so you can change 
+the appearance and behavior of the visualizations. For example, you might want 
+to see which airline has the lowest average fares.
+
+. In the side navigation, click *Recently viewed* and open the *Global Flight Dashboard*. 
+. In the menu bar, click *Edit*.
+. In the *Average Ticket Price* visualization, click the gear icon in 
+the upper right.
+. From the *Options* menu, select *Edit visualization*.
++
+*Average Ticket Price* is a metric visualization.
+To specify which groups to display 
+in this visualization, you use an {es} {ref}/search-aggregations.html[bucket aggregation]. 
+This aggregation sorts the documents that match your search criteria into different 
+categories, or buckets.
+
+[float]
+==== Create a bucket aggregation
+
+. In the *Buckets* pane, select *Add > Split group*.
+. In the *Aggregation* dropdown, select *Terms*.
+. In the *Field* dropdown, select *Carrier*.
+. Set *Descending* to *4*.
+. Click *Apply changes* image:images/apply-changes-button.png[].
++
+You now see the average ticket price for all four airlines.
++
+[role="screenshot"]
+image::images/tutorial-sample-edit1.png[]
+
+[float]
+==== Save the visualization
+
+. In the menu bar, click *Save*.
+. Leave the visualization name as is and confirm the save.
+. Go to the *Global Flight* dashboard and scroll the *Average Ticket Price* visualization to see the four prices.
+. Optionally, edit the dashboard. Resize the panel 
+for the *Average Ticket Price* visualization by dragging the
+handle in the lower right. You can also rearrange the visualizations by clicking 
+the header and dragging. Be sure to save the dashboard.
++
+[role="screenshot"]
+image::images/tutorial-sample-edit2.png[]
+
+[float]
+[[tutorial-sample-inspect]]
+=== Inspect the data
+
+Seeing visualizations of your data is great, 
+but sometimes you need to look at the actual data to
+understand what's really going on. You can inspect the data behind any visualization
+and view the {es} query used to retrieve it.
+
+. In the dashboard, hover the pointer over the pie chart, and then click the icon in the upper right. 
+. From the *Options* menu, select *Inspect*.
++
+The initial view shows the document count. 
++
+[role="screenshot"]
+image::images/tutorial-sample-inspect1.png[]
+
+. To look at the query used to fetch the data for the visualization, select *View > Requests*
+in the upper right of the Inspect pane.
+
+[float]
+[[tutorial-sample-remove]]
+=== Remove the sample data set
+When you’re done experimenting with the sample data set, you can remove it. 
+
+. Go to the *Sample data* page. 
+. On the *Sample flight data* card, click *Remove*.
+
+[float]
+=== Next steps
+
+Now that you have a handle on the {kib} basics, you might be interested in the 
+tutorial <<tutorial-build-dashboard, Build your own dashboard>>, where you'll learn to:
+
+* Load data
+* Define an index pattern
+* Discover and explore data
+* Create visualizations
+* Add visualizations to a dashboard
+
+
+
+

docs/getting-started/tutorial-sample-discover.asciidoc

@@ -1,27 +0,0 @@
-[[tutorial-sample-discover]]
-=== Using Discover
-
-In the Discover application, the Flight data is presented in a table. You can 
-interactively explore the data, including searching and filtering.
-
-* In the side navigation, select *Discover*.
-
-The current index pattern appears below the filter bar. An
-<<index-patterns, index pattern>> tells {kib} which {es} indices you want to 
-explore.
-
-The `kibana_sample_data_flights` index contains a time field. A histogram
-shows the distribution of documents over time. 
-
-[role="screenshot"]
-image::images/tutorial-sample-discover1.png[]
-
-By default, all fields are shown for each matching document. To choose which fields to display, 
-hover the pointer over the the list of *Available Fields* and then click *add* next 
-to each field you want include as a column in the table.
-
-For example, if you add the `DestAirportID` and `DestWeather` fields, 
-the display includes columns for those two fields:
-
-[role="screenshot"]
-image::images/tutorial-sample-discover2.png[]

docs/getting-started/tutorial-sample-edit.asciidoc

@@ -1,45 +0,0 @@
-[[tutorial-sample-edit]]
-=== Editing a visualization
-
-You have edit permissions for the *Global Flight* dashboard so you can change 
-the appearance and behavior of the visualizations. For example, you might want 
-to see which airline has the lowest average fares.
-
-. Go to the *Global Flight* dashboard. 
-. In the menu bar, click *Edit*.
-. In the *Average Ticket Price* visualization, click the gear icon in 
-the upper right.
-. From the *Options* menu, select *Edit visualization*.
-
-==== Edit a metric visualization 
-
-*Average Ticket Price* is a metric visualization.
-To specify which groups to display 
-in this visualization, you use an {es} {ref}/search-aggregations.html[bucket aggregation]. 
-This aggregation sorts the documents that match your search criteria into different 
-categories, or buckets.
-
-. In the *Buckets* pane, select *Split Group*.
-. In the *Aggregation* dropdown menu, select *Terms*.
-. In the *Field* dropdown, select *Carrier*.
-. Set *Descending* to four.
-. Click *Apply changes* image:images/apply-changes-button.png[].
-
-You now see the average ticket price for all four airlines.
-
-[role="screenshot"]
-image::images/tutorial-sample-edit1.png[]
-
-==== Save the changes
-
-. In the menu bar, click *Save*.
-. Leave the visualization name unchanged and click *Save*.
-. Go to the *Global Flight* dashboard. 
-. Resize the panel for the *Average Ticket Price* visualization by dragging the
-handle in the lower right.  
-You can also rearrange the visualizations by clicking the header and dragging.
-. In the menu bar, click *Save* and then confirm the save.
-+
-[role="screenshot"]
-image::images/tutorial-sample-edit2.png[]
-

docs/getting-started/tutorial-sample-filter.asciidoc

@@ -1,23 +0,0 @@
-[[tutorial-sample-filter]]
-=== Filtering the data
-
-Many visualizations in the *Global Flight* dashboard are interactive. You can 
-apply filters to modify the view of the data across all visualizations.
-
-. In the *Controls* visualization, set an *Origin City* and a *Destination City*.
-. Click *Apply changes*.
-+
-The `OriginCityName` and the `DestCityName` fields are filtered to match 
-the data you specified. 
-+
-For example, this dashboard shows the data for flights from London to Newark
-and Pittsburgh. 
-+
-[role="screenshot"]
-image::images/tutorial-sample-filter.png[]
-+
-. To remove the filters, in the *Controls* visualization, click *Clear form*, and then
-*Apply changes*.
-
-You can also add filters manually.  In the filter bar, click *Add a Filter*
-and specify the data you want to view.

docs/getting-started/tutorial-sample-inspect.asciidoc

@@ -1,24 +0,0 @@
-[[tutorial-sample-inspect]]
-=== Inspecting the data
-
-Seeing visualizations of your data is great, 
-but sometimes you need to look at the actual data to
-understand what's really going on. You can inspect the data behind any visualization
-and view the {es} query used to retrieve it.
-
-. Hover the pointer over the *Flight Count and Average Ticket Price* visualization.
-. Click the icon in the upper right. 
-. From the *Options* menu, select *Inspect*.
-+
-[role="screenshot"]
-image::images/tutorial-sample-inspect1.png[]
-
-You can also look at the query used to fetch the data for the visualization. 
-
-. Open the *View: Data* menu and select *Requests*. 
-. Click the tabs to look at the request statistics, the Elasticsearch request, 
-and the response in JSON. 
-. To close the editor, click X in the upper right.
-+
-[role="screenshot"]
-image::images/tutorial-sample-inspect2.png[]

docs/getting-started/tutorial-sample-query.asciidoc

@@ -1,30 +0,0 @@
-[[tutorial-sample-query]]
-=== Querying the data
-
-You can enter an {es} query to narrow the view of the data.
-
-. To find all flights out of Rome, submit this query:
-+
-[source,text]
-OriginCityName:Rome   
-
-. For a more complex query with AND and OR, try this:
-+
-[source,text]
-OriginCityName:Rome AND (Carrier:JetBeats OR "Kibana Airlines")
-+
-The dashboard updates to show data for the flights out of Rome on JetBeats and 
-{kib} Airlines.  
-+
-[role="screenshot"]
-image::images/tutorial-sample-query.png[]
-
-. When you are finished exploring the dashboard, remove the query by 
-clearing the contents in the query bar and pressing Enter.
-
-In general, filters are faster than queries. For more information, see {ref}/query-filter-context.html[Query and filter context].
-
-TIP: {kib} has an experimental autocomplete feature that can 
-help jumpstart your queries. To turn on this feature, click *Options* on the 
-right of the query bar and opt in. With autocomplete enabled, 
-search suggestions are displayed when you start typing your query.  

docs/getting-started/tutorial-sample-remove.asciidoc

@@ -1,18 +0,0 @@
-[[tutorial-sample-remove]]
-=== Wrapping up
-
-When you’re done experimenting with the sample data set, you can remove it. 
-
-. Go to the {kib} home page and click the link next to  *Sample data*. 
-. On the *Sample flight data* card, click *Remove*.
-
-Now that you have a handle on the {kib} basics, you might be interested in:
-
-* <<tutorial-build-dashboard, Building your own dashboard>>.  You’ll learn how to load your own 
-data, define an index pattern, and create visualizations and dashboards. 
-* <<visualize>>. You’ll find information about all the visualization types 
-{kib} has to offer.
-* <<dashboard>>. You have the ability to share a dashboard, or embed the dashboard in a web page.
-* <<discover>>. You'll learn more about searching data and filtering by field.
-
-

docs/getting-started/tutorial-visualizing.asciidoc

@@ -1,46 +1,48 @@
 [[tutorial-visualizing]]
-=== Visualizing your data
+=== Visualize your data
 
 In the Visualize application, you can shape your data using a variety
-of charts, tables, and maps, and more. You'll create four
-visualizations: a pie chart, bar chart, coordinate map, and Markdown widget.
+of charts, tables, and maps, and more. In this tutorial, you'll create four
+visualizations: 
 
-. Open *Visualize.*
-. Click *Create a visualization* or the *+* button. You'll see all the visualization
+* <<tutorial-visualize-pie, Pie chart>>
+* <<tutorial-visualize-bar, Bar chart>>
+* <<tutorial-visualize-map, Coordinate map>>
+* <<tutorial-visualize-markdown, Markdown widget>>
+
+[float]
+[[tutorial-visualize-pie]]
+=== Pie chart
+
+You'll use the pie chart to
+gain insight into the account balances in the bank account data.
+
+. Open *Visualize* to show the overview page.
+. Click *Create new visualization*. You'll see all the visualization
 types in Kibana.
 +
 [role="screenshot"]
 image::images/tutorial-visualize-wizard-step-1.png[]
 . Click *Pie*.
 
-. In *New Search*, select the `ba*` index pattern. You'll use the pie chart to
-gain insight into the account balances in the bank account data.
+. In *Choose a source*, select the `ba*` index pattern. 
 +
-[role="screenshot"]
-image::images/tutorial-visualize-wizard-step-2.png[]
-
-=== Pie chart
-
 Initially, the pie contains a single "slice."
 That's because the default search matched all documents.
-
-[role="screenshot"]
-image::images/tutorial-visualize-pie-1.png[]
-
++
 To specify which slices to display in the pie, you use an Elasticsearch
 {ref}/search-aggregations.html[bucket aggregation]. This aggregation
 sorts the documents that match your search criteria into different
-categories, also known as _buckets_.
-
-Use a bucket aggregation to establish
+categories. You'll use a bucket aggregation to establish
 multiple ranges of account balances and find out how many accounts fall into
 each range.
 
-. In the *Buckets* pane, click *Split Slices.*
-. In the *Aggregation* dropdown menu, select *Range*.
-. In the *Field* dropdown menu, select *balance*.
-. Click *Add Range* four times to bring the total number of ranges to six.
-. Define the following ranges:
+. In the *Buckets* pane, click *Add > Split slices.*
++
+.. In the *Aggregation* dropdown, select *Range*.
+.. In the *Field* dropdown, select *balance*.
+.. Click *Add range* four times to bring the total number of ranges to six.
+.. Define the following ranges:
 +
 [source,text]
 0             999
@@ -51,120 +53,117 @@ each range.
 31000       50000
 
 . Click *Apply changes* image:images/apply-changes-button.png[].
-
++
 Now you can see what proportion of the 1000 accounts fall into each balance
 range.
-
++
 [role="screenshot"]
 image::images/tutorial-visualize-pie-2.png[]
 
-Add another bucket aggregation that looks at the ages of the account
+. Add another bucket aggregation that looks at the ages of the account
 holders.
 
-. At the bottom of the *Buckets* pane, click *Add sub-buckets*.
-. In *Select buckets type,* click *Split Slices*.
-. In the *Sub Aggregation* dropdown, select *Terms*.
-. In the *Field* dropdown, select *age*.
-. Click  *Apply changes* image:images/apply-changes-button.png[].
+.. At the bottom of the *Buckets* pane, click *Add*.
+.. For *sub-bucket type,* select *Split slices*.
+.. In the *Sub aggregation* dropdown, select *Terms*.
+.. In the *Field* dropdown, select *age*.
 
+. Click  *Apply changes* image:images/apply-changes-button.png[].
++
 Now you can see the break down of the ages of the account holders, displayed
 in a ring around the balance ranges.
-
++
 [role="screenshot"]
 image::images/tutorial-visualize-pie-3.png[]
 
-To save this chart so you can use it later:
-
-Click *Save* in the top menu bar and enter `Pie Example`.
+. To save this chart so you can use it later, click *Save* in 
+the top menu bar and enter `Pie Example`.
 
+[float]
+[[tutorial-visualize-bar]]
 === Bar chart
 
 You'll use a bar chart to look at the Shakespeare data set and compare
 the number of speaking parts in the plays.
 
-Create a *Vertical Bar* chart and set the search source to `shakes*`.
-
+. Create a *Vertical Bar* chart and set the search source to `shakes*`.
++
 Initially, the chart is a single bar that shows the total count
 of documents that match the default wildcard query.
 
-[role="screenshot"]
-image::images/tutorial-visualize-bar-1.png[]
+. Show the number of speaking parts per play along the Y-axis.
 
-Show the number of speaking parts per play along the Y-axis.
-This requires you to configure the Y-axis
-{ref}/search-aggregations.html[metric aggregation.]
-This aggregation computes metrics based on values from the search results.
+.. In the *Metrics* pane, expand *Y-axis*.
+.. Set *Aggregation* to *Unique Count*.
+.. Set *Field* to *speaker*.
+.. In the *Custom label* box, enter `Speaking Parts`.
 
-. In the *Metrics* pane, expand *Y-Axis*.
-. Set *Aggregation* to *Unique Count*.
-. Set *Field* to *speaker*.
-. In the *Custom Label* box, enter `Speaking Parts`.
 . Click  *Apply changes* image:images/apply-changes-button.png[].
 
+. Show the plays along the X-axis.
 
+.. In the *Buckets* pane, click *Add > X-axis*.
+.. Set *Aggregation* to *Terms*.
+.. Set *Field* to *play_name*.
+.. To list plays alphabetically, in the *Order* dropdown, select *Ascending*.
+.. Give the axis a custom label, `Play Name`.
+
+. Click  *Apply changes* image:images/apply-changes-button.png[].
++
 [role="screenshot"]
 image::images/tutorial-visualize-bar-1.5.png[]
-
-
-Show the plays along the X-axis.
-
-. In the *Buckets* pane, click *X-Axis*.
-. Set *Aggregation* to *Terms* and *Field* to *play_name*.
-. To list plays alphabetically, in the *Order* dropdown menu, select *Ascending*.
-. Give the axis a custom label, `Play Name`.
-. Click  *Apply changes* image:images/apply-changes-button.png[].
-
+. *Save* this chart with the name `Bar Example`.
++
 Hovering over a bar shows a tooltip with the number of speaking parts for
 that play.
-
++
 Notice how the individual play names show up as whole phrases, instead of
 broken into individual words. This is the result of the mapping
 you did at the beginning of the tutorial, when you marked the `play_name` field
 as `not analyzed`.
 
-*Save* this chart with the name `Bar Example`.
-
+[float]
+[[tutorial-visualize-map]]
 === Coordinate map
 
 Using a coordinate map, you can visualize geographic information in the log file sample data.
 
 . Create a *Coordinate map* and set the search source to `logstash*`.
-. In the top menu bar, click the time picker on the far right.
-. Click *Absolute*.
-. Set the start time to May 18, 2015 and the end time to May 20, 2015.
-. Click *Go*.
-
++
 You haven't defined any buckets yet, so the visualization is a map of the world.
 
-[role="screenshot"]
-image::images/tutorial-visualize-map-1.png[]
+. Set the time.
+.. In the time filter, click *Show dates*.
+.. Click the start date, then *Absolute*.
+.. Set the *Start date* to May 18, 2015.
+.. In the time filter, click *now*, then *Absolute*.
+.. Set the *End date* to May 20, 2015.
 
-Now map the geo coordinates from the log files.
+. Map the geo coordinates from the log files.
+
+.. In the *Buckets* pane, click *Add > Geo coordinates*.
+.. Set *Aggregation* to *Geohash*.
+.. Set *Field* to *geo.coordinates*.
 
-. In the *Buckets* pane, click *Geo Coordinates*.
-. Set *Aggregation* to *Geohash* and *Field* to *geo.coordinates*.
 . Click *Apply changes* image:images/apply-changes-button.png[].
-
++
 The map now looks like this:
-
++
 [role="screenshot"]
 image::images/tutorial-visualize-map-2.png[]
 
-You can navigate the map by clicking and dragging.  The controls
-on the top left of the map enable you to zoom the map and set filters.
-Give them a try.
-
-[role="screenshot"]
-image::images/tutorial-visualize-map-3.png[]
-
-*Save* this map with the name `Map Example`.
+. Navigate the map by clicking and dragging.  Use the controls
+on the left to zoom the map and set filters.
+. *Save* this map with the name `Map Example`.
 
+[float]
+[[tutorial-visualize-markdown]]
 === Markdown
 
 The final visualization is a Markdown widget that renders formatted text.
 
 . Create a *Markdown* visualization.
-. In the text box, enter the following:
+. Copy the following text into the text box.
 +
 [source,markdown]
 # This is a tutorial dashboard!
@@ -172,10 +171,10 @@ The Markdown widget uses **markdown** syntax.
 > Blockquotes in Markdown use the > character.
 
 . Click *Apply changes* image:images/apply-changes-button.png[].
-
-The Markdown renders in the preview pane:
-
++
+The Markdown renders in the preview pane.
++
 [role="screenshot"]
 image::images/tutorial-visualize-md-2.png[]
 
-*Save* this visualization with the name `Markdown Example`.
+. *Save* this visualization with the name `Markdown Example`.

docs/getting-started/wrapping-up.asciidoc

@@ -1,14 +0,0 @@
-[[wrapping-up]]
-=== Wrapping up
-
-Now that you have a handle on the basics, you're ready to start exploring
-your own data with Kibana.
-
-* See {kibana-ref}/discover.html[Discover] for information about searching and filtering
-your data.
-* See {kibana-ref}/visualize.html[Visualize] for information about the visualization
-types Kibana has to offer.
-* See {kibana-ref}/management.html[Management] for information about configuring Kibana
-and managing your saved objects.
-* See {kibana-ref}/console-kibana.html[Console] to learn about the interactive
-console you can use to submit REST requests to Elasticsearch.