# Introducing Transformations

## **Introducing Transformations**

Transformations are used to perform a variety of operations on data as it moves through the dataflow pipeline. Astera Data Stack provides a full complement of built-in transformations enabling you to build sophisticated data maps.

Astera Data Stack transformations are divided into two types— record-level and set-level.

## **Record-level Transformations**

Record-level transformations are used to create derived values by applying a *Lookup*, *Function*, or *Expression* to fields from a single record.

Examples of record-level transformations include *Lookups*, *Expressions*, and *Function* transformations.

In Astera, data records flow between set transformations. Record-level transformations are used to transform or augment individual fields during these movements.

A record-level transformation can be connected to only one set transformation. For instance, a *Lookup* or *Expression* cannot receive input from two different set transformations.

## **Set-level Transformations**

Set-level transformations, on the other hand, operate on a group of records and may result in the Joining, Reordering, Eliminating, or aggregating of records. Examples of set transformations include *Join*, *Sort*, *Route*, *Distinct*, etc. *Data Sources* and *Destinations* are also considered set transformations.

Other than transformations that enable combining multiple data sources—such as *Join*, *Merge*, and *Union*—transformations can receive input from only one set transformation. Transformations, however, can receive input from any number of record-level transformations as long as all these record-level transformations receive input from the same transformation.