[DOCS] Addresses feedback on data frame intro. (#369)

This PR:
* Addresses feedback.
* Amends terminology.

Author: szabosteve

docs/en/stack/ml/dataframes.asciidoc

@@ -3,45 +3,46 @@
 
 beta[]
 
-A _{dataframe}_ is a transformation of a dataset by certain rules. You can think 
-of it like a spreadsheet or a data table that makes your data ready to be analyzed 
-and organized.
+A _{dataframe}_ is a transformation of data that has been indexed in elasticsearch. 
+Use data frames to _pivot_ your data into a new entity centric index for example. 
+By transforming and summarizing your data, it becomes possible to visualize and 
+analyze it in alternative and interesting ways.
 
-A lot of {es} datasets are organized as a stream of events: each event is a individual 
+A lot of {es} indices are organized as a stream of events: each event is an individual 
 document, for example a single item purchase. {dataframe-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 (see 
 the example below).
 
-The {dataframe} feature enables you to define a _pivot_ which is a set of features 
-that transform the dataset into a different, more digestible format. Pivoting 
-results in a summary of your dataset (which is the {dataframe} itself).
+The {dataframe} feature enables 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} itself).
 
 Defining a pivot consist of two main parts. First, you select one or more fields 
-that your dataset will be grouped by. Principally you can select categorical 
+that your data will be grouped by. Principally you can select categorical 
 fields (terms) for grouping. You can also select numerical fields, in this case, 
 the field values will be bucketed using an interval you specify.
 
 The second step is deciding how you want to aggregate the grouped data. When 
-using aggregations, you practically ask questions about the dataset. There are 
+using aggregations, you practically ask questions about the index. There are 
 different types of aggregations, each with its own purpose and output. To learn 
 more about the supported aggregations and group-by fields, see 
 {ref}/data-frame-transform-pivot.html[Pivot resources].
 
 As an optional step, it's also possible to add a query to further limit the 
 scope of the aggregation.
 
-IMPORTANT: In 7.2, you can build {dataframes} on the top of a static dataset. 
+IMPORTANT: In 7.2, you can build {dataframes} on the top of a static indices. 
 When new data comes into the index, you have to perform the transformation again 
 on the altered data.
 
 .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 {dataframe} that is grouped by the product categories (women's shoes, men's
@@ -53,6 +54,4 @@ shows the number of sold items in every product category in the last year.
 image::ml/images/ml-dataframepivot.jpg["Example of a data frame pivot in {kib}"]
 
 IMPORTANT: Creating a {dataframe} leaves your source index intact. A new index will 
-be created dedicated to the {dataframe}.
-
-TIP: Using {dataframes} does not require {dfeeds}.
+be created dedicated to the {dataframe}.