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[DOCS] Refresh transform screenshots with histograms #59264

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17 changes: 9 additions & 8 deletions docs/reference/transform/ecommerce-tutorial.asciidoc
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
[role="xpack"]
png[role="xpack"]
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[testenv="basic"]
[[ecommerce-transforms]]
=== Tutorial: Transforming the eCommerce sample data
Expand Down Expand Up @@ -28,7 +28,8 @@ might want to derive from this eCommerce 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!
like, so go ahead and play with it! You can also enable histogram charts to get
a better understanding of the distribution of values in your 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
Expand All @@ -38,11 +39,11 @@ 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?

Go to *Management* > *Elasticsearch* > *Transforms* in {kib} and use the
wizard to create a {transform}:
Go to *Management* > *Stack Management* > *Data* > *Transforms* in {kib} and use
the wizard to create a {transform}:

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

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I would retake this screenshot to use a selection of columns which show off the histogram charts better - specifically try and include some numeric fields. For example:

image

This selection also includes the numeric taxless_total_price and total_quantity which you use in the example transform. The distribution chart of the country_iso_codes also looks nice!

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Thanks @peteharverson I've updated the screenshots with the columns you suggested front and centre


In this case, we grouped the data by customer ID and calculated the sum of
products each customer purchased.
Expand All @@ -59,7 +60,7 @@ We'll accomplish this by using the
on the `order_id` field:

[role="screenshot"]
image::images/ecommerce-pivot2.jpg["Adding multiple aggregations to a {transform} in {kib}"]
image::images/ecommerce-pivot2.png["Adding multiple aggregations to a {transform} in {kib}"]

TIP: If you're interested in a subset of the data, you can optionally include a
<<request-body-search-query,query>> element. In this
Expand Down Expand Up @@ -220,7 +221,7 @@ can stop it.
You can start, stop, and manage {transforms} in {kib}:

[role="screenshot"]
image::images/manage-transforms.jpg["Managing {transforms} in {kib}"]
image::images/manage-transforms.png["Managing {transforms} in {kib}"]

Alternatively, you can use the
<<start-transform,start {transforms}>> and
Expand Down Expand Up @@ -249,7 +250,7 @@ data is ingested.
For example, use the *Discover* application in {kib}:

[role="screenshot"]
image::images/ecommerce-results.jpg["Exploring the new index in {kib}"]
image::images/ecommerce-results.png["Exploring the new index in {kib}"]

--

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2 changes: 1 addition & 1 deletion docs/reference/transform/overview.asciidoc
Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ quantity. The result is an entity-centric index that shows the number of sold
items in every product category in the last year.

[role="screenshot"]
image::images/pivot-preview.jpg["Example of a {transform} pivot in {kib}"]
image::images/pivot-preview.png["Example of a {transform} pivot in {kib}"]

IMPORTANT: The {transform} leaves your source index intact. It
creates a new index that is dedicated to the transformed data.
Expand Down