Transformations and Properties

Refer to this page for a description of each transformation, sample outputs, and any special conditions to keep in mind while configuring the filters and settings.

Tips

• To select multiple fields or Events, select the operator IN and separate the values with a comma.

Using Special Keywords in Transformations

The $self Keyword

Use the variable $self to apply a formula on multiple fields on which the transformation is being applied.

For example, use the following filters to update the value of the amount and amount_in_home_currency fields:

Filters: For Event Type = deals, and Event Field archived = false, and Field Names in amount, amount_in_home_currency.

Settings: Enter value or formula = 1.2* $self.

The transformation is applied to both the fields as follows:

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The $null Keyword

The $null keyword can be used when you want to add fields or update existing field values with a null value. As all null fields are dropped when Hevo ingests your data, you can create transformations with this keyword to include them again. Or, you may want to assign null values to fields based on some conditions rather than dropping them altogether.

The following sequence of two transformations illustrate the usage of the $null field.

1. Change Field Value

For Event Type = catalog.book and Event Field = price, change the value of price to $null. The field with its null value is not displayed in the test results.

2. Add a Field

Now, in the Events transformed in step 1 above, for Event Field = price and Event Field Value Is Null, add a field count with Value, Out-of-Stock. The transformation is applied successfully andcan be verified in the results, confirming the behavior of the $null keyword.

The settings appear as follows:

The final output of the two transformations appears as:

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CONTROL BLOCKS

If-Else

Description:	Add an If-Else block to branch the data based on conditions. You can then add respective transformation blocks for the True and False condition. If-Else differs from the normal filters, as you can create two transformations based on the If-Else evaluation.
Filter By:	All Events, Event Type, Event Field
Configuration Settings:	None
Transformation Considerations:	- The If-Else block branches the code to apply the transformation. You cannot currently merge the branches subsequently, and have to write the transformation for both branches separately. - To delete the IF-Else block, you must first delete its branches.

Example:

If the author is Galos, Mike, a new field status with value Discontinued is added. Else, the field price is renamed to discounted price and its value is changed to 0.9*price. The $self keyword is used to indicate the price field.

Filters:

Results The results in both conditions are displayed below:

If True:

If False:

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DATE FUNCTIONS

Change Datetime Field Values

Description:	Add or subtract the specified amount of time from the given date value.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	Datetime Field. For example, Month. Offset Value. Use a negative value to subtract the time. For example, 1 to move the month forward by 1, or -2 to move the month back by 2. See Datetime Field Values
Transformation Considerations:	- The date must be a valid date object.

Example:

The behavior of this transformation is illustrated in two steps:

• Transformation 1: Parse Date from String.

  Filters: Event Type = sale_events and Field Name = packing_date.

  This transformation changes the string date value 22-07-1999 to date value 932601600000 (the millisecond representation of the Java date object).

• Transformation 2: Change Datetime Value

  Filters: Field Name = packing_date.

  Settings: Datetiem Unit = Year and Amount = 5

  This increases the year value by 5 years as 1090454400000 which translates to 22-07-2004 in String format.

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Format Date to String

Description:	Format a datetime value to a string value.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	String Format - Use capital M to denote month (DD-MM-YYYY), and small m to denote minutes. - Refer to this page for the list of supported patterns and formats. - Automatic detection of all date fields in an Event is not currently supported.
Transformation Considerations:	None.

Example:

Filters: Event Type = deals. Filter on Field Name = createdate.

The value is converted into string format.

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Parse Date from String

Description:	Parse a string value to a datetime object.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	String Format. The string format in which the date is currently stored.
Transformation Considerations:	- Refer to this page for the list of supported patterns and formats.

Example:

Filters: Event Type = deals. Filter on Field Name = createdate.

Settings: String Format (in which the date exists) = DD-MM-YYYY.

The string value is converted to a date object.

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EVENT MANIPULATION

Add a Field

Description:	Create a new field in the Event
Filter By:	All Events, Event Type, Event Field
Configuration Settings:	- Name of the Field - Value of the Field
Transformation Considerations:	You can filter by only one Event Type and Property at a time.

Example:

Filters: All Events + Field Name = price + Field Value greater than $20.

Settings: New Field Name = discount and New Field Value = 0.15*price.

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Change Field Values

Description:	Replace a field value with another value.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	Enter formula or value: Provide the new value for the field(s).
Transformation Considerations:	None.

Example:

Filters: All Event Types where the value of the archived field = false.

Settings: Enter formula or value = 500.

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Drop Events

Description:	Filter Events based on a criteria. Matching Events are dropped from the table.
Filter By:	All Events, Event Type, Event Field
Configuration Settings:	None.
Transformation Considerations:	None.

Example:

Filters: Event Type = audience and All fields where Field Name = discount_band and Field Value > 10

Settings: None.

The Event is dropped and a confirmation message is displayed during test.

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Drop Fields

Description:	Filter column values based on a criteria. Matching field columns are dropped from the table.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	None.
Transformation Considerations:	None.

Example:

Filters: Event Type = deals. Filter fields with Field Name = closed.

Settings: None.

The selected field is dropped.

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Find & Replace

Description:	Find and replace string values across one or more fields.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	- Find. The string to be searched. For example, Limited. - Replace. The value to replace the search string with. For example, Ltd. - Regex. Select the check box if you want the input to be matched using a regular expression. For example, “\D” to find all non-digit characters. - Match Case. Select the check box to match the casing of the string while searching.
Transformation Considerations:	Find & Replace works on field values only and not on field or Event names.

Example:

Filters: Event Type = audience and All fields where Field Name = discount_band

Settings: Regex = selected. Find = “\d”. Replace = 0.

All digits found in discount_band field are changed to 0.

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Rename Events

Description:	Rename one or more Events.
Filter By:	All Events, Event Type, Event Field
Configuration Settings:	New Name. Specify the new name for the Event(s).
Transformation Considerations:	You can rename only one Event at a time.

Example:

Filters: Event Type = deals. Filter on Field Name = hs_is_closed and Field Value = false.

Settings: New Name = ChangedEvent.

The Event name is changed if hs_is_closed is false.

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Rename Fields

Description:	Rename one or more fields.
Filter By:	All Events, Event Type, Event Field
Configuration Settings:	- Field Name. The existing name of the field. New Name. The changed name of the field.
Transformation Considerations:	You can rename only one field at a time.

Example:

Filters: Event Type = catalog.book and Event Field Name =genre and Event Field Value = Computer

Settings: Field Name = price and New Name = original_price

The name of the field is changed

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JSON MANIPULATION

Flatten JSON

Description:	Flatten a JSON object into individual fields.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	None.
Transformation Considerations:	The fields must be in valid JSON format, else an error is displayed.

Example:

Filters: Event Type = catalog.books. Field Name = category.

Settings: None.

Each value of the category field is converted to a separate JSON field.

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Parse JSON from String

Description:	Parse JSON string to a JSON object
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	None.
Transformation Considerations:	None.

Example:

Filters: Event Type = demo.agents and Field Name = category

Settings: None

The JSON string is changed to JSON fields.

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MATHEMATICAL FUNCTIONS

Format Number to String

Description:	Format numbers to strings.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	String Format. This can be: - Plain Text (“123456”) - Number (1,23,456) - Percent (123.45 %) - Scientific (1.01E + 12) - Currency ($ 1,23,456.00). Currently only USD is supported. - Custom
Transformation Considerations:	None.

Example:

Filters: Event Type = company and Field Name = monthly_spend

Settings: String Format = currency

The numeric monthly spend value is changed to a currency format.

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Parse Number from String

Description:	Parse number from string value.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	None.
Transformation Considerations:	Any commas and spaces are removed from the string.

Example:

Filters: Event Type = catalog.book and Field Name = copies

Settings: None

The value of the copies field is changed from String to Number format.

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Round-off Decimal Fields

Description:	Round-off Decimal Values to lower precision.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	Rounding Mode. This can be Round Up, Round Down, Round Half Up, Round Half Down, Celing and Floor. The rounding mode is applied in conjunction with the decimal scale. Scale: The number of decimal places to retain after rounding. Not applicable for Ceiling and Floor modes. See Rounding Modes
Transformation Considerations:	Scale is defined for all rounding modes except Ceiling and Floor.

Example:

Filters: Event Type = deals and Event Field Name = hs_forecast_amount.

Settings: Rounding mode = Floor

The value of hs_forecast_amount is rounded off using the Floor rounding mode.

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SECURITY-RELATED TRANSFORMATIONS

Hash Fields

Description:	Hash a sensitive field value.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	Hashing Algorithm. This can be MD5, SHA-256, SHA-512.
Transformation Considerations:	None.

Example:

Filters: All Events. Filter on Field Name = price.

Settings: Hashing Algorith = SHA-256

The value of the price field is hashed using SHA-256 algorithm.

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Mask Fields

Description:	Mask a sensitive field value.
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	None.
Transformation Considerations:	None.

Example:

Filters: Event Type = advertisable. Filter on Field Name = status and Field Value = approved.

Settings: None.

The value of the field is masked.

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STRING FUNCTIONS

Modify Text Casing

Description:	Convert string values to lowercase, uppercase, snake case, or title case.
Filter By:	Convert string values to lowercase, uppercase, snake case, or title case.
Configuration Settings:	Text Case. This can be To Lower Case, To Upper Case, To Title Case, To Snake Case. See Text Casing
Transformation Considerations:	None.

Example:

Filters: Event Type = email_events. Filter on Field Name = type.

Settings: Text Case = Snake Case

The value of the field is changed to Snake case.

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Split Fields

Description:	Split a field in to two or more fields
Filter By:	All Events, Event Type, Event Field, All Fields, Field Name, Field Value
Configuration Settings:	Delimiter. The regular expression used to find the string around which the field will should be split. - To specify a regular expression special character as delimiter, escape it using . For example to use $ as a delimiter in Donald$Duck specify \$ as the delimiter. - Trailing empty strings are discarded. For example, in the case of wonder_women_ with _ as delimiter, the output is "wonder", "women" instead of "wonder", "women",. Create fields from values. If selected, the returned array of strings after splitting is converted into separate fields in the Event.
Transformation Considerations:	None

Example:

Filters: Event Type = products. Filter on Field Name = recurringbillingfrequency

Settings: Delimiter = Comma. Create fields from values = Select this to create a new field for each value.

The settings appear as follows:

The output is modified as follows if the Create fields from value check box is selected:

The output is modified as follows if the Create fields from value check box is NOT selected: