[role="xpack"]
[testenv="basic"]
[[ecommerce-dataframes]]
=== Transforming the eCommerce sample data

beta[]

<<ml-dataframes,{dataframe-transforms-cap}>> enable you to retrieve information
from an {es} index, transform it, and store it in another 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-transform}:

[role="screenshot"]
image::images/ecommerce-pivot1.jpg["Creating a simple {dataframe-transform} 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-transform} in {kib}"]

TIP: If you're interested in a subset of the data, you can optionally include a
{ref}/search-request-body.html#request-body-search-query[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 want to use more complex queries, you can create your {dataframe} from a
{kibana-ref}/save-open-search.html[saved search].

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": {
      "bool": {
        "filter": {
          "term": {"currency": "EUR"}
        }
      }
    }
  },
  "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.

.. Decide whether you want the {dataframe-transform} to run once or continuously.
--
+
--
Since this sample data index is unchanging, let's use the default behavior and
just run the {dataframe-transform} once.

[role="screenshot"]
image::images/ecommerce-batch.jpg["Specifying the {dataframe-transform} options in {kib}"]

If you want to try it out, however, go ahead and click on *Continuous mode*. 
You must choose a field that the {dataframe-transform} can use to check which
entities have changed. In general, it's a good idea to use the ingest timestamp
field. In this example, however, you can use the `order_date` field.

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": {
      "bool": {
        "filter": {
          "term": {
            "currency": "EUR"
          }
        }
      }
    }
  },
  "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"
        }
      }
    }
  },
  "dest": {
    "index": "ecommerce-customers"
  }
}
--------------------------------------------------
// CONSOLE
// TEST[skip:setup kibana sample data]
--

. Start the {dataframe-transform}.
+
--

TIP: Even though resource utilization is automatically adjusted based on the
cluster load, a {dataframe-transform} increases search and indexing load on your
cluster while it runs. If you're experiencing an excessive load, however, you
can stop it.

You can start, stop, and manage {dataframe-transforms} in {kib}:

[role="screenshot"]
image::images/dataframe-transforms.jpg["Managing {dataframe-transforms} 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 and {kib} index
patterns remain.
