[DOCS] Adds quick start (#78822)

* [DOCS] Getting started refresh

* Dashboard changes

* Discover and Dashboard changes

* [DOCS] Adds quick start

* Redirects

* Redirects pt 2

* Redirects pt

* More redirect issues

* Removed second chunk of KQL tasks

* Review comments

docs/getting-started/quick-start-guide.asciidoc

@@ -0,0 +1,142 @@
+[[get-started]]
+== Quick start
+
+To quickly get up and running with {kib}, set up on Cloud, then add a sample data set that you can explore and analyze.
+
+When you've finished, you'll know how to:
+
+* <<explore-the-data,Explore the data with *Discover*.>>
+
+* <<view-and-analyze-the-data,Gain insight into the data with *Dashboard*.>>
+
+[float]
+=== Before you begin 
+When security is enabled, you must have `read`, `write`, and `manage` privileges on the `kibana_sample_data_*` indices. For more information, refer to {ref}/security-privileges.html[Security privileges].
+
+[float]
+[[set-up-on-cloud]]
+== Set up on cloud
+
+include::{docs-root}/shared/cloud/ess-getting-started.asciidoc[]
+
+[float]
+[[gs-get-data-into-kibana]]
+== Add the sample data
+
+Sample data sets come with sample visualizations, dashboards, and more to help you explore {kib} without adding your own data.
+
+. From the home page, click *Try our sample data*.
+
+. On the *Sample eCommerce orders* card, click *Add data*.
++
+[role="screenshot"]
+image::getting-started/images/add-sample-data.png[]
+
+[float]
+[[explore-the-data]]
+== Explore the data
+
+*Discover* displays an interactive histogram that shows the distribution of of data, or documents, over time, and a table that lists the fields for each document that matches the index. By default, all fields are shown for each matching document.
+
+. Open the menu, then click *Discover*.
+
+. Change the <<set-time-filter, time filter>> to *Last 7 days*.
++
+[role="screenshot"]
+image::images/tutorial-discover-2.png[]
+
+. To focus in on the documents you want to view, use the <<kuery-query,{kib} Query Language>>. In the *KQL* search field, enter:
++
+[source,text]
+products.taxless_price >= 60 AND category : Women's Clothing
++
+The query returns the women's clothing orders for $60 and more.
++
+[role="screenshot"]
+image::images/tutorial-discover-4.png[]
+
+. Hover over the list of *Available fields*, then click *+* next to the fields you want to view in the table.
++
+For example, when you add the *category* field, the table displays the product categories for the orders.
++
+[role="screenshot"]
+image::images/tutorial-discover-3.png[]
++
+For more information, refer to <<discover, *Discover*>>.
+
+[float]
+[[view-and-analyze-the-data]]
+== View and analyze the data
+
+A dashboard is a collection of panels that you can use to view and analyze the data. Panels contain visualizations, interactive controls, Markdown, and more.
+
+. Open the menu, then click *Dashboard*. 
+
+. Click *[eCommerce] Revenue Dashboard*.
++
+[role="screenshot"]
+image::getting-started/images/tutorial-sample-dashboard.png[]
+
+[float]
+[[filter-and-query-the-data]]
+=== Filter the data
+
+To focus in on the data you want to view on the dashboard, use filters. 
+
+. From the *Controls* visualization, make a selection from the *Manufacturer* and *Category* dropdowns, then click *Apply changes*.
++
+For example, the following dashboard shows the data for women's clothing from Gnomehouse. 
++
+[role="screenshot"]
+image::getting-started/images/tutorial-sample-filter.png[]
+
+. To manually add a filter, click *Add filter*, then specify the options.
++
+For example, to view the orders for Wednesday, select *day_of_week* from the *Field* dropdown, select *is* from the *Operator* dropdown, then select *Wednesday* from the *Value* dropdown.
++
+[role="screenshot"]
+image::getting-started/images/tutorial-sample-filter2.png[]
+
+. When you are done, remove the filters.
++
+For more information, refer to <<dashboard,*Dashboard*>>.
+
+[float]
+[[create-a-visualization]]
+=== Create a visualization
+
+To create a treemap that shows the top regions and manufacturers, use *Lens*, then add the treemap to the dashboard.
+
+. From the {kib} toolbar, click *Edit*, then click *Create new*.
+
+. On the *New Visualization* window, click *Lens*.
+
+. From the *Available fields* list, drag and drop the following fields to the visualization builder:
+
+* *geoip.city_name*
+
+* *manufacturer.keyword*
++
+. From the visualization dropdown, select *Treemap*.
++
+[role="screenshot"]
+image::getting-started/images/tutorial-visualization-dropdown.png[Visualization dropdown with Treemap selected]
+
+. Click *Save*.
+
+. On the *Save Lens visualization*, enter a title and make sure *Add to Dashboard after saving* is selected, then click *Save and return*.
++
+The treemap appears as the last visualization on the dashboard.
++
+[role="screenshot"]
+image::getting-started/images/tutorial-final-dashboard.gif[Final dashboard with new treemap visualization]
++
+For more information, refer to <<lens, *Lens*>>.
+
+[float]
+[[quick-start-whats-next]]
+== What's next?
+
+If you are you ready to add your own data, refer to <<connect-to-elasticsearch,Add data to {kib}>>.
+
+If you want to ingest your data, refer to {ingest-guide}/ingest-management-getting-started.html[Quick start: Get logs and metrics into the Elastic Stack].

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

@@ -1,56 +0,0 @@
-[[tutorial-define-index]]
-=== Define your index patterns
-
-Index patterns tell {kib} which {es} indices you want to explore.
-An index pattern can match the name of a single index, or include a wildcard
-(*) 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*`.
-
-[float]
-==== Create the index patterns
-
-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.
-
-. Open the menu, then go to *Stack Management > {kib} > Index Patterns*.
-
-. If this is your first index pattern, the *Create index pattern* page opens.
-
-. In the *Index pattern name* field, enter `shakes*`.
-+
-[role="screenshot"]
-image::images/tutorial-pattern-1.png[Image showing how to enter shakes* in Index Pattern Name field]
-
-. Click *Next step*.
-
-. On the *Configure settings* page, *Create index pattern*.
-+
-You’re presented a table of all fields and associated data types in the index.
-
-. Create a second index pattern named  `ba*`. 
-
-[float]
-==== Create an index pattern for the time series data
-
-Create an index pattern for the Logstash index, which
-contains the time series data.
-
-. Create an index pattern named `logstash*`, then click *Next step*.
-
-. From the *Time field* dropdown, select *@timestamp, then click *Create index pattern*.
-+
-[role="screenshot"]
-image::images/tutorial_index_patterns.png[Image showing how to create an 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 if the indices are
-available, open the menu, go to *Dev Tools > Console*, then enter `GET _cat/indices`. Alternately, use
-`curl -XGET "http://localhost:9200/_cat/indices"`.
-For Windows, run `Invoke-RestMethod -Uri "http://localhost:9200/_cat/indices"` in Powershell.
-
-

docs/getting-started/tutorial-discovering.asciidoc

@@ -1,35 +0,0 @@
-[[explore-your-data]]
-=== Explore your data
-
-With *Discover*, you use {ref}/query-dsl-query-string-query.html#query-string-syntax[Elasticsearch
-queries] to explore your data and narrow the results with filters.
-
-. Open the menu, then go to *Discover*.
-+
-The `shakes*` index pattern appears.
-
-. To make `ba*` the index, click the *Change Index Pattern* dropdown, then select `ba*`.
-+
-By default, all fields are shown for each matching document.
-
-. In the *Search* field, enter the following, then click *Update*:
-+
-[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. Results appear for account numbers 8, 32, 78, 85, and 97.
-+
-[role="screenshot"]
-image::images/tutorial-discover-2.png[Image showing the search results for account numbers between zero and 99, with balances in excess of 47,500]
-+
-. Hover over the list of *Available fields*, then
-click *Add* next to each field you want include in the table.
-+
-For example, when you add the `account_number` field, the display changes to a list of five
-account numbers.
-+
-[role="screenshot"]
-image::images/tutorial-discover-3.png[Image showing a dropdown with five account numbers, which match the previous query for account balance]
-
-Now that you know what your documents contain, it's time to gain insight into your data with visualizations.

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

@@ -1,219 +0,0 @@
-[[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
-
-Alternatively, for Windows users, run the following commands in Powershell:
-
-[source,shell]
-Invoke-RestMethod https://download.elastic.co/demos/kibana/gettingstarted/8.x/shakespeare.json -OutFile shakespeare.json
-Invoke-RestMethod https://download.elastic.co/demos/kibana/gettingstarted/8.x/accounts.zip -OutFile accounts.zip
-Invoke-RestMethod https://download.elastic.co/demos/kibana/gettingstarted/8.x/logs.jsonl.gz -OutFile 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 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

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

@@ -1,159 +0,0 @@
-[[explore-kibana-using-sample-data]]
-== Explore {kib} using sample data
-
-Ready to get some hands-on experience with {kib}?
-In this tutorial, you’ll work with {kib} sample data and learn to:
-
-* <<explore-the-data, Explore the sample data using *Discover*>>
-
-* <<view-and-analyze-the-data, View and analyze the data on a dashboard>>
-
-* <<filter-and-query-the-data, Filter and query the dashboard data>>
-
-NOTE: If security is enabled, you must have `read`, `write`, and `manage` privileges
-on the `kibana_sample_data_*` indices. For more information, refer to
-{ref}/security-privileges.html[Security privileges].
-
-[float]
-[[add-the-sample-data]]
-=== Add the sample data
-
-Add the *Sample flight data*.
-
-. On the home page, click *Load a data set and a {kib} dashboard*. 
-
-. On the *Sample flight data* card, click *Add data*.
-
-[float]
-[[explore-the-data]]
-=== Explore the data
-
-Explore the documents in the index that 
-match the selected index pattern. The index pattern tells {kib} which {es} index you want to
-explore. 
-
-. Open the menu, then go to *Discover*.
-
-. Make sure `kibana_sample_data_flights` is the current index pattern. 
-You might need to click *New* in the {kib} toolbar to refresh the data.
-+
-You'll see a histogram that shows the distribution of 
-documents over time. A table lists the fields for 
-each document that matches the index. By default, all fields are shown.
-+
-[role="screenshot"]
-image::getting-started/images/tutorial-sample-discover1.png[]
-
-. Hover over the list of *Available fields*, then click *Add* next 
-to each field you want explore in the table.
-+
-[role="screenshot"]
-image::getting-started/images/tutorial-sample-discover2.png[]
-
-[float]
-[[view-and-analyze-the-data]]
-=== View and analyze the data
-
-A _dashboard_ is a collection of panels that provide you with an overview of your data that you can 
-use to analyze your data. Panels contain everything you need, including visualizations, 
-interactive controls, Markdown, and more.
-
-To open the *Global Flight* dashboard, open the menu, then go to *Dashboard*.
-
-[role="screenshot"]
-image::getting-started/images/tutorial-sample-dashboard.png[]
-
-[float]
-[[change-the-panel-data]]
-==== Change the panel data
-
-To gain insights into your data, change the appearance and behavior of the panels. 
-For example, edit the metric panel to find the airline that has the lowest average fares.
-
-. In the {kib} toolbar, click *Edit*.
-
-. In the *Average Ticket Price* metric panel, open the panel menu, then select *Edit visualization*.
-
-. To change the data on the panel, use an {es} {ref}/search-aggregations.html[bucket aggregation],  
-which sorts the documents that match your search criteria into different categories or buckets.
-
-.. In the *Buckets* pane, select *Add > Split group*.
-
-.. From the *Aggregation* dropdown, select *Terms*.
-
-.. From the *Field* dropdown, select *Carrier*.
-
-.. Set *Descending* to *4*, then click *Update*.
-+
-The average ticket price for all four airlines appear in the visualization builder.
-+
-[role="screenshot"]
-image::getting-started/images/tutorial-sample-edit1.png[]
-
-. To save your changes, click *Save and return* in the {kib} toolbar. 
-
-. To save the dashboard, click *Save* in the {kib} toolbar.
-+
-[role="screenshot"]
-image::getting-started/images/tutorial-sample-edit2.png[]
-
-[float]
-[[filter-and-query-the-data]]
-==== Filter and query the data
-
-To focus in on the data you want to explore, use filters and queries. 
-For more information, refer to 
-{ref}/query-filter-context.html[Query and filter context].
-
-To filter the data:
-
-. In the *Controls* visualization, select an *Origin City* and *Destination City*, then click *Apply changes*.
-+
-The `OriginCityName` and the `DestCityName` fields filter the data in the panels. 
-+
-For example, the following dashboard shows the data for flights from London to Milan. 
-+
-[role="screenshot"]
-image::getting-started/images/tutorial-sample-filter.png[]
-
-. To manually add a filter, click *Add filter*,
-then specify the data you want to view.
-
-. When you are finished experimenting, remove all filters.
-
-[[query-the-data]]
-To query the data:
-
-. To view all flights out of Rome, enter the following in the *KQL* query bar, then click *Update*:
-+
-[source,text]
-OriginCityName: Rome
-
-. For a more complex query with AND and OR, enter:
-+
-[source,text]
-OriginCityName:Rome AND (Carrier:JetBeats OR Carrier:"Kibana Airlines")
-+
-The dashboard panels update to display the flights out of Rome on JetBeats and 
-{kib} Airlines.  
-+
-[role="screenshot"]
-image::getting-started/images/tutorial-sample-query.png[]
-
-. When you are finished exploring, remove the query by 
-clearing the contents in the *KQL* query bar, then click *Update*.
-
-[float]
-=== Next steps
-
-Now that you know the {kib} basics, try out the <<create-your-own-dashboard, Create your own dashboard>> tutorial, where you'll learn to:
-
-* Add a data set to {kib}
-
-* Define an index pattern
-
-* Discover and explore data
-
-* Create and add panels to a dashboard
-
-

docs/getting-started/tutorial-visualizing.asciidoc

@@ -1,193 +0,0 @@
-[[tutorial-visualizing]]
-=== Visualize your data
-
-Shape your data using a variety
-of {kib} supported visualizations, tables, and more. In this tutorial, you'll create four
-visualizations that you'll use to create a dashboard.
-
-To begin, open the menu, go to *Dashboard*, then click *Create new dashboard*.
-
-[float]
-[[compare-the-number-of-speaking-parts-in-the-play]]
-=== Compare the number of speaking parts in the plays
-
-To visualize the Shakespeare data and compare the number of speaking parts in the plays, create a bar chart using *Lens*.
-
-. Click *Create new*, then click *Lens* on the *New Visualization* window.
-+
-[role="screenshot"]
-image::images/tutorial-visualize-wizard-step-1.png[Image showing different options for your new visualization]
-
-. Make sure the index pattern is *shakes*.
-
-. Display the play data along the x-axis.
-
-.. From the *Available fields* list, drag and drop *play_name* to the *X-axis* field.
-
-.. Click *Top values of play_name*.
-
-.. From the *Order direction* dropdown, select *Ascending*.
-
-.. In the *Label* field, enter `Play Name`. 
-
-. Display the number of speaking parts per play along the y-axis.
-
-.. From the *Available fields* list, drag and drop *speaker* to the *Y-axis* field.
-
-.. Click *Unique count of speaker*.
-
-.. In the *Label* field, enter `Speaking Parts`. 
-+
-[role="screenshot"]
-image::images/tutorial-visualize-bar-1.5.png[Bar chart showing the speaking parts  data]
-
-. *Save* the chart with the name `Bar Example`.
-+
-To show a tooltip with the number of speaking parts for that play, hover over a bar.
-+
-Notice how the individual play names show up as whole phrases, instead of
-broken up 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`.
-
-[float]
-[[view-the-average-account-balance-by-age]]
-=== View the average account balance by age
-
-To gain insight into the account balances in the bank account data, create a pie chart. In this tutorial, you'll use the {es}
-{ref}/search-aggregations.html[bucket aggregation] to specify the pie slices to display. The bucket aggregation sorts the documents that match your search criteria into different
-categories and establishes multiple ranges of account balances so that you can find how many accounts fall into each range.
-
-. Click *Create new*, then click *Pie* on the *New Visualization* window.
-
-. On the *Choose a source* window, select `ba*`.
-+
-Since the default search matches all documents, the pie contains a single slice. 
-
-. In the *Buckets* pane, click *Add > Split slices.*
-
-.. From the *Aggregation* dropdown, select *Range*.
-
-.. From the *Field* dropdown, select *balance*.
-
-.. Click *Add range* until there are six rows of fields, then define the following ranges:
-+
-[source,text]
-0             999
-1000         2999
-3000         6999
-7000        14999
-15000       30999
-31000       50000
-
-. Click *Update*.
-+
-The pie chart displays the proportion of the 1,000 accounts that fall into each of the ranges.
-+
-[role="screenshot"]
-image::images/tutorial-visualize-pie-2.png[Pie chart displaying accounts that fall into each of the ranges, scaled to 1000 accounts]
-
-. Add another bucket aggregation that displays the ages of the account holders.
-
-.. In the *Buckets* pane, click *Add*, then click *Split slices*.
-
-.. From the *Sub aggregation* dropdown, select *Terms*.
-
-.. From the *Field* dropdown, select *age*, then click *Update*.
-+
-The break down of the ages of the account holders are displayed
-in a ring around the balance ranges.
-+
-[role="screenshot"]
-image::images/tutorial-visualize-pie-3.png[Final pie chart showing all of the changes]
-
-. Click *Save*, then enter `Pie Example` in the *Title* field.
-
-[float]
-[role="xpack"]
-[[visualize-geographic-information]]
-=== Visualize geographic information
-
-To visualize geographic information in the log file data, use <<maps,Maps>>.
-
-. Click *Create new*, then click *Maps* on the *New Visualization* window.
-
-. To change the time, use the time filter.
-
-.. Set the *Start date* to `May 18, 2015 @ 12:00:00.000`.
-
-.. Set the *End date* to `May 20, 2015 @ 12:00:00.000`.
-+
-[role="screenshot"]
-image::images/gs_maps_time_filter.png[Image showing the time filter for Maps tutorial]
-
-.. Click *Update*
-
-. Map the geo coordinates from the log files.
-
-.. Click *Add layer > Clusters and grids*.
-
-.. From the *Index pattern* dropdown, select *logstash*.
-
-.. Click *Add layer*.
-
-. Specify the *Layer Style*.
-
-.. From the *Fill color* dropdown, select the yellow to red color ramp.
-
-.. In the *Border width* field, enter `3`.
-
-.. From the *Border color* dropdown, select *#FFF*, then click *Save & close*.
-+
-[role="screenshot"]
-image::images/tutorial-visualize-map-2.png[Example of a map visualization]
-
-. Click *Save*, then enter `Map Example` in the *Title* field.
-
-. Add the map to your dashboard.
-
-.. Open the menu, go to *Dashboard*, then click *Add*. 
-
-.. On the *Add panels* flyout, click *Map Example*.
-
-[float]
-[[tutorial-visualize-markdown]]
-=== Add context to your visualizations with Markdown
-
-Add context to your new visualizations with Markdown text.
-
-. Click *Create new*, then click *Markdown* on the *New Visualization* window.
-
-. In the *Markdown* text field, enter:
-+
-[source,markdown]
-# This is a tutorial dashboard!
-The Markdown widget uses **markdown** syntax.
-> Blockquotes in Markdown use the > character.
-
-. Click *Update*.
-+
-The Markdown renders in the preview pane.
-+
-[role="screenshot"]
-image::images/tutorial-visualize-md-2.png[Image showing example markdown editing field]
-
-. Click *Save*, then enter `Markdown Example` in the *Title* field.
-
-[role="screenshot"]
-image::images/tutorial-dashboard.png[Final visualization with bar chart, pie chart, map, and markdown text field]
-
-[float]
-=== Next steps
-
-Now that you have the basics, you're ready to start exploring your own system data with {kib}.
-
-* To add your own data to {kib}, refer to <<connect-to-elasticsearch,Add data to {kib}>>.
-
-* To search and filter your data, refer to {kibana-ref}/discover.html[Discover].
-
-* To create a dashboard with your own data, refer to <<dashboard, Dashboard>>.
-
-* To create maps that you can add to your dashboards, refer to <<maps,Maps>>.
-
-* To create presentations of your live data, refer to <<canvas,Canvas>>.

docs/management/rollups/create_and_manage_rollups.asciidoc

@@ -67,7 +67,7 @@ You can read more at {ref}/rollup-job-config.html[rollup job configuration].
 === Try it: Create and visualize rolled up data
 
 This example creates a rollup job to capture log data from sample web logs.
-To follow along, add the <<gs-get-data-into-kibana, sample web logs data set>>.
+To follow along, add the sample web logs data set.
 
 In this example, you want data that is older than 7 days in the target index pattern `kibana_sample_data_logs`
 to roll up once a day into the index `rollup_logstash`. You’ll bucket the

docs/redirects.asciidoc

@@ -59,7 +59,7 @@ This page has moved. Please see <<reporting-getting-started>>.
 [role="exclude",id="add-sample-data"]
 == Add sample data
 
-This page has moved. Please see <<gs-get-data-into-kibana>>.
+This page has moved. Please see <<get-started>>.
 
 [role="exclude",id="tilemap"]
 == Coordinate map
@@ -112,3 +112,9 @@ This content has moved. See
 
 This content has moved. See
 {ref}/ccr-getting-started.html#ccr-getting-started-remote-cluster[Connect to a remote cluster].
+
+[role="exclude",id="tutorial-define-index"]
+== Define your index patterns
+
+This content has moved. See
+<<get-started, Quick start>>.

docs/setup/connect-to-elasticsearch.asciidoc

@@ -11,7 +11,7 @@ To start working with your data in {kib}, you can:
 
 * Connect {kib} with existing {es} indices.
 
-If you're not ready to use your own data, you can add a <<gs-get-data-into-kibana, sample data set>>
+If you're not ready to use your own data, you can add a <<get-started, sample data set>>
 to see all that you can do in {kib}.
 
 [float]

docs/user/dashboard/dashboard-drilldown.asciidoc

@@ -39,7 +39,7 @@ Create the *Host Overview* drilldown shown above.
 
 *Set up the dashboards*
 
-. Add the <<gs-get-data-into-kibana, sample web logs>> data set.
+. Add the sample web logs data set.
 
 . Create a new dashboard, called `Host Overview`, and include these visualizations
 from the sample data set:

docs/user/dashboard/url-drilldown.asciidoc

@@ -36,7 +36,7 @@ The following panels support URL drilldowns:
 
 This example shows how to create the "Show on Github" drilldown shown above.
 
-. Add the <<gs-get-data-into-kibana, sample web logs>> data set.
+. Add the sample web logs data set.
 . Open the *[Logs] Web traffic* dashboard. This isn’t data from Github, but it should work for demonstration purposes.
 . In the dashboard menu bar, click *Edit*.
 . In *[Logs] Visitors by OS*, open the panel menu, and then select *Create drilldown*.

docs/user/getting-started.asciidoc

@@ -1,61 +0,0 @@
-[[get-started]]
-= Get started
-
-[partintro]
---
-
-Ready to try out {kib} and see what it can do? The quickest way to get started with {kib} is to set up on Cloud, then add a sample data set to explore the full range of {kib} features.
-
-[float]
-[[set-up-on-cloud]]
-== Set up on cloud
-
-include::{docs-root}/shared/cloud/ess-getting-started.asciidoc[]
-
-[float]
-[[gs-get-data-into-kibana]]
-== Get data into {kib}
-
-The easiest way to get data into {kib} is to add a sample data set.
-
-{kib} has several sample data sets that you can use before loading your own data:
-
-* *Sample eCommerce orders* includes visualizations for tracking product-related information,
-such as cost, revenue, and price.
-
-* *Sample flight data* includes visualizations for monitoring flight routes.
-
-* *Sample web logs* includes visualizations for monitoring website traffic.
-
-To use the sample data sets:
-
-. Go to the home page.
-
-. Click *Load a data set and a {kib} dashboard*.
-
-. Click *View data* and view the prepackaged dashboards, maps, and more.
-
-[role="screenshot"]
-image::getting-started/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 change to reflect the most recent installation.
-
-[float]
-== Next steps
-
-* To get a hands-on experience creating visualizations, follow the <<explore-kibana-using-sample-data, add sample data>> tutorial.
-
-* If you're ready to load an actual data set and build a dashboard, follow the <<create-your-own-dashboard, Create your own dashboard>> tutorial.
-
---
-
-include::{kib-repo-dir}/getting-started/tutorial-sample-data.asciidoc[]
-
-include::{kib-repo-dir}/getting-started/tutorial-full-experience.asciidoc[]
-
-include::{kib-repo-dir}/getting-started/tutorial-define-index.asciidoc[]
-
-include::{kib-repo-dir}/getting-started/tutorial-discovering.asciidoc[]
-
-include::{kib-repo-dir}/getting-started/tutorial-visualizing.asciidoc[]

docs/user/index.asciidoc

@@ -2,6 +2,8 @@ include::introduction.asciidoc[]
 
 include::whats-new.asciidoc[]
 
+include::{kib-repo-dir}/getting-started/quick-start-guide.asciidoc[]
+
 include::setup.asciidoc[]
 
 include::monitoring/configuring-monitoring.asciidoc[leveloffset=+1]
@@ -11,8 +13,6 @@ include::monitoring/monitoring-kibana.asciidoc[leveloffset=+2]
 
 include::security/securing-kibana.asciidoc[]
 
-include::getting-started.asciidoc[]
-
 include::discover.asciidoc[]
 
 include::dashboard/dashboard.asciidoc[]

docs/user/introduction.asciidoc

@@ -155,6 +155,6 @@ and start exploring data in minutes.
 You can also <<install, install {kib} on your own>> &mdash; no code, no additional
 infrastructure required.
 
-Our <<create-your-own-dashboard, Getting Started>> and in-product guidance can
+Our <<get-started, Quick start>> and in-product guidance can
 help you get up and running, faster. Click the help icon image:images/intro-help-icon.png[]
 in the top navigation bar for help with questions or to provide feedback.

docs/user/security/rbac_tutorial.asciidoc

@@ -28,7 +28,7 @@ To complete this tutorial, you'll need the following:
 *  **A space**: In this tutorial, use `Dev Mortgage` as the space
 name. See <<spaces-managing, spaces management>> for
 details on creating a space.
-*  **Data**:  You can use <<gs-get-data-into-kibana, sample data>> or
+*  **Data**:  You can use <<get-started, sample data>> or
 live data.  In the following steps, Filebeat and Metricbeat data are used.
 
 [float]