Introducing Function Transformations

The Function Transformation functionality in Astera Data Stack is used to perform various data manipulation transformations, wherein Function Transformation objects are used to transform data based on certain logic. Astera Data Stack has a library of 290+ built-in functions which can be accessed from the Function Transformations section in the Toolbox.

Note: Function Transformations are available as objects in Astera Data Stack, where each function can be dragged-and-dropped onto the designer, allowing for direct mapping and manipulation of data from preceding objects.

These Transformation objects in Astera Data Stack help in data manipulations such as, string manipulation, data conversion, calculations, data parsing, comparisons, and date/time manipulation etc.

Upon expanding the Function Transformations tab in the Toolbox, various expandable tabs will open, each consisting of categorized Function Transformation objects. These tabs consist of the following sections:

Conversion

Functions in this section are used to convert a value from one data type to another.

DateTime

Functions in this section provide distinct ways in which date and time values can be formatted, read, and/or displaye

DateTimeWithOffset

Functions in this section provide distinct ways in which date and time values can be formatted, read, and/or displayed, specifically considering DateTime offset values.

Encoding

Functions in this section help users in encoding and decoding data values using various parameters.

Files

Functions in this section allow users to manipulate data in files, also allowing users to extract files’ metadata.

Financial

Functions in this section are used to evaluate financial information. The transformed values help users in making crucial finance-related business decisions.

GUID

The only function in this section, NewGuid, allows users to generate a new Globally Unique Identifier (GUID).

Logical

Functions in this section mainly allow users to apply logical reasonings to data, such as classifying data under different datatypes and applying ‘if conditions’ to existing data.

Matching

Functions in this section allow users to apply phonetic algorithms to data.

Math

Functions in this section help with mathematical calculations.

Name and Address

Functions in this section allow for the parsing of string (Name and Address) values, which are then split and returned in accordance with defined and/or built-in parameters.

Processes

Functions in this section allow users to obtain information regarding processes, moreover, to terminate running processes.

QA

Functions in this section are mainly used for testing purposes, making sure that syntax errors etc. are resolved, and different parameters are valid.

Regular Expressions

Functions in this section allow users with RegEx matching and validation instances.

String

Functions in this section help in manipulating string values, enabling users to alter the ways in which these values are displayed.

TimeSpan

Functions in this section are used to return durations in accordance with different DateTime parameters, such as Minutes, Ticks, Days, etc.

To use any Function Transformation object in a dataflow, simply drag-and-drop from the Toolbox onto the designer, and map fields from and to the object, as required.