OpenSearch/docs/reference/transform/overview.asciidoc

[role="xpack"]
[[transform-overview]]
=== {transform-cap} overview
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<titleabbrev>Overview</titleabbrev>
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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. {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.

{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 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
for grouping. If you use numerical fields, the field values are bucketed using
an interval that you specify.

The second step is deciding how you want to aggregate the grouped data. When 
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 
<<put-transform>>.

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 or continuously. 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.

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 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}"]

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


[[transform-performance]]
==== Performance considerations

{transforms-cap} perform search aggregations on the source indices then index
the results into the destination index. Therefore, a {transform} never takes
less time or uses less resources than the aggregation and indexing processes. 

If your {transform} must process a lot of historic data, it has high resource
usage initially--particularly during the first checkpoint.

For better performance, make sure that your search aggregations and queries are 
optimized and that your {transform} is processing only necessary data.