[DOCS] Removes data frame leftovers from transforms overview (#49434)

docs/reference/transform/overview.asciidoc

@@ -7,22 +7,19 @@
 
 beta[]
 
-A _{dataframe}_ is a two-dimensional tabular data structure. In the context of
-the {stack}, it is a transformation of data that is indexed in {es}. For
-example, you can use {dataframes} to _pivot_ your data into a new entity-centric
-index. By transforming and summarizing your data, it becomes possible to
-visualize and analyze it in alternative and interesting ways.
+You can use {transforms} to _pivot_ your data into a new entity-centric index. 
+By transforming and summarizing your data, it becomes possible to visualize and 
+analyze it in alternative and interesting ways.
 
 A lot of {es} indices are organized as a stream of events: each event is an 
-individual document, for example a single item purchase. {dataframes-cap} enable
+individual document, for example a single item purchase. {transforms-cap} enable
 you to summarize this data, bringing it into an organized, more
 analysis-friendly format. For example, you can summarize all the purchases of a
 single customer.
 
-You can create {dataframes} by using {transforms}.
 {transforms-cap} enable you to define a pivot, which is a set of
 features that transform the index into a different, more digestible format.
-Pivoting results in a summary of your data, which is the {dataframe}.
+Pivoting results in a summary of your data in a new index.
 
 To define a pivot, first you select one or more fields that you will use to
 group your data. You can select categorical fields (terms) and numerical fields
@@ -38,34 +35,32 @@ more about the supported aggregations and group-by fields, see
 As an optional step, you can also add a query to further limit the scope of the
 aggregation.
 
-The {transform} performs a composite aggregation that 
-paginates through all the data defined by the source index query. The output of
-the aggregation is stored in a destination index. Each time the 
-{transform} queries the source index, it creates a _checkpoint_. You 
-can decide whether you want the {transform} to run once (batch 
-{transform}) or continuously ({transform}). A batch 
-{transform} is a single operation that has a single checkpoint. 
-{ctransforms-cap} continually increment and process checkpoints as new 
-source data is ingested.
+The {transform} performs a composite aggregation that paginates through all the 
+data defined by the source index query. The output of the aggregation is stored 
+in a destination index. Each time the {transform} queries the source index, it 
+creates a _checkpoint_. You can decide whether you want the {transform} to run 
+once (batch {transform}) or continuously ({transform}). A batch {transform} is a 
+single operation that has a single checkpoint. {ctransforms-cap} continually 
+increment and process checkpoints as new source data is ingested.
 
 .Example
 
-Imagine that you run a webshop that sells clothes. Every order creates a document 
-that contains a unique order ID, the name and the category of the ordered product, 
-its price, the ordered quantity, the exact date of the order, and some customer 
-information (name, gender, location, etc). Your dataset contains all the transactions 
-from last year.
+Imagine that you run a webshop that sells clothes. Every order creates a 
+document that contains a unique order ID, the name and the category of the 
+ordered product, its price, the ordered quantity, the exact date of the order, 
+and some customer information (name, gender, location, etc). Your dataset 
+contains all the transactions from last year.
 
 If you want to check the sales in the different categories in your last fiscal
-year, define a {transform} that groups the data by the product
-categories (women's shoes, men's clothing, etc.) and the order date. Use the
-last year as the interval for the order date. Then add a sum aggregation on the
-ordered quantity. The result is a {dataframe} that shows the number of sold
+year, define a {transform} that groups the data by the product categories 
+(women's shoes, men's clothing, etc.) and the order date. Use the last year as 
+the interval for the order date. Then add a sum aggregation on the ordered 
+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/ml-dataframepivot.jpg["Example of a data frame pivot in {kib}"]
 
 IMPORTANT: The {transform} leaves your source index intact. It
-creates a new index that is dedicated to the {dataframe}.
+creates a new index that is dedicated to the transformed data.