[DOCS] Adds data frame eCommerce example

docs/en/stack/data-frames/ecommerce-example.asciidoc

@@ -0,0 +1,245 @@
+[role="xpack"]
+[testenv="basic"]
+[[ecommerce-dataframes]]
+== Transforming your data with {dataframes}
+++++
+<titleabbrev>Transforming your data</titleabbrev>
+++++
+
+beta[]
+
+<<ml-dataframes,{dataframes-cap}>> enable you to retrieve information from an
+{es} index, transform it, and store it in a new index. Let's use the
+{kibana-ref}/add-sample-data.html[{kib} sample data] to demonstrate how you can
+pivot and summarize your data with {dataframe-transforms}.
+
+
+. If the {es} {security-features} are enabled, obtain a user ID with sufficient
+privileges to complete these steps. 
++
+--
+You need `manage_data_frame_transforms` cluster privileges to preview and create
+{dataframe-transforms}. Members of the built-in `data_frame_transforms_admin`
+role have these privileges.
+
+You also need `read` and `view_index_metadata` index privileges on the source
+index and `read`, `create_index`, and `index` privileges on the destination
+index. 
+
+For more information, see <<security-privileges>> and <<built-in-roles>>.
+--
+
+. Choose your _source index_.
++
+--
+In this example, we'll use the eCommerce orders sample data. If you're not
+already familiar with the `kibana_sample_data_ecommerce` index, use the
+*Revenue* dashboard in {kib} to explore the data. Consider what insights you
+might want to derive from this eCommerce data.
+--
+
+. Play with various options for grouping and aggregating the data. 
++
+--
+For example, you might want to group the data by product ID and calculate the
+total number of sales for each product and its average price. Alternatively, you
+might want to look at the behavior of individual customers and calculate how
+much each customer spent in total and how many different categories of products
+they purchased. Or you might want to take the currencies or geographies into
+consideration. What are the most interesting ways you can transform and
+interpret this data?
+
+_Pivoting_ your data involves using at least one field to group it and applying
+at least one aggregation. You can preview what the transformed data will look
+like, so go ahead and play with it!
+
+For example, go to *Machine Learning* > *Data Frames* in {kib} and use the
+wizard to create a {dataframe}:
+
+[role="screenshot"]
+image::images/ecommerce-pivot1.jpg["Creating a simple {dataframe} in {kib}"]
+
+In this case, we grouped the data by customer ID and calculated the sum of
+products each customer purchased.
+
+Let's add some more aggregations to learn more about our customers' orders. For
+example, let's calculate the total sum of their purchases, the maximum number of
+products that they purchased in a single order, and their total number of orders.
+We'll accomplish this by using the
+{ref}/search-aggregations-metrics-sum-aggregation.html[`sum` aggregation] on the
+`taxless_total_price` field, the
+{ref}/search-aggregations-metrics-max-aggregation.html[`max` aggregation] on the
+`total_quantity` field, and the
+{ref}/search-aggregations-metrics-cardinality-aggregation.html[`cardinality` aggregation]
+on the `order_id` field:
+
+[role="screenshot"]
+image::images/ecommerce-pivot2.jpg["Adding multiple aggregations to a {dataframe} in {kib}"]
+
+TIP: If you're interested in a subset of the data, you can optionally include a
+{ref}/search-request-query.html[query] element. In this example, we've filtered
+the data so that we're only looking at orders with a `currency` of `EUR`.
+Alternatively, we could group the data by that field too.
+
+If you prefer, you can use the
+{ref}/preview-data-frame-transform.html[preview {dataframe-transforms} API]:
+
+[source,js]
+--------------------------------------------------
+POST _data_frame/transforms/_preview
+{
+  "source": {
+    "index": "kibana_sample_data_ecommerce",
+    "query": {
+      "query_string": {
+        "query": "currency : EUR",
+        "default_operator": "AND"
+      }
+    }
+  },
+  "pivot": {
+    "group_by": {
+      "customer_id": {
+        "terms": {
+          "field": "customer_id"
+        }
+      }
+    },
+    "aggregations": {
+      "total_quantity.sum": {
+        "sum": {
+          "field": "total_quantity"
+        }
+      },
+      "taxless_total_price.sum": {
+        "sum": {
+          "field": "taxless_total_price"
+        }
+      },
+      "total_quantity.max": {
+        "max": {
+          "field": "total_quantity"
+        }
+      },
+      "order_id.cardinality": {
+        "cardinality": {
+          "field": "order_id"
+        }
+      }
+    }
+  }
+}
+--------------------------------------------------
+// CONSOLE
+// TEST[skip:set up sample data]
+--
+
+. When you are satisfied with what you see in the preview, create the
+{dataframe-transform}. 
++
+--
+Supply a job ID and the name of the target (or _destination_) index. In {kib},
+you can choose to create and start the job, create it, or copy it to the
+clipboard.
+
+If you prefer, you can use the
+{ref}/put-data-frame-transform.html[create {dataframe-transforms} API]. For
+example:
+
+[source,js]
+--------------------------------------------------
+PUT _data_frame/transforms/ecommerce-customer-transform
+{
+  "source": {
+    "index": [
+      "kibana_sample_data_ecommerce"
+    ],
+    "query": {
+      "query_string": {
+        "query": "currency : EUR",
+        "default_operator": "AND"
+      }
+    }
+  },
+  "dest": {
+    "index": "ecommerce-customers"
+  },
+  "pivot": {
+    "group_by": {
+      "customer_id": {
+        "terms": {
+          "field": "customer_id"
+        }
+      }
+    },
+    "aggregations": {
+      "total_quantity.sum": {
+        "sum": {
+          "field": "total_quantity"
+        }
+      },
+      "taxless_total_price.sum": {
+        "sum": {
+          "field": "taxless_total_price"
+        }
+      },
+      "total_quantity.max": {
+        "max": {
+          "field": "total_quantity"
+        }
+      },
+      "order_id.cardinality": {
+        "cardinality": {
+          "field": "order_id"
+        }
+      }
+    }
+  }
+}
+--------------------------------------------------
+// CONSOLE
+// TEST[skip:setup kibana sample data]
+--
+
+. Start the {dataframe-transform}.
++
+--
+
+TIP: A {dataframe-transform} increases search and indexing load on your cluster
+while it runs. When it reaches the end of the data in your index, it stops
+automatically. If you're experiencing an excessive load, however, you can stop
+it sooner.
+
+You can start, stop, and manage {dataframe} jobs in {kib}:
+
+[role="screenshot"]
+image::images/dataframe-jobs.jpg["Managing {dataframe} jobs in {kib}"]
+
+Alternatively, you can use the
+{ref}/start-data-frame-transform.html[start {dataframe-transforms}] and
+{ref}/stop-data-frame-transform.html[stop {dataframe-transforms}] APIs. For
+example:
+
+[source,js]
+--------------------------------------------------
+POST _data_frame/transforms/ecommerce-customer-transform/_start
+--------------------------------------------------
+// CONSOLE
+// TEST[skip:setup kibana sample data]
+
+--
+
+. Explore the data in your new index.
++
+--
+For example, use the *Discover* application in {kib}:
+
+[role="screenshot"]
+image::images/ecommerce-results.jpg["Exploring the new index in {kib}"]
+
+--
+
+TIP: If you do not want to keep the {dataframe-transform}, you can delete it in
+{kib} or use the
+{ref}/delete-data-frame-transform.html[delete {dataframe-transform} API]. When
+you delete a {dataframe-transform}, its destination index remains.

docs/en/stack/data-frames/index.asciidoc

@@ -1,3 +1,3 @@
 include::dataframes.asciidoc[]
-//include::{es-repo-dir}/data-frames/pivoting.asciidoc[]
+include::ecommerce-example.asciidoc[]
 include::api-quickref.asciidoc[]