tStandardizeDate Standard properties

Schema and Edit schema

• A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.

• Built-In: You create and store the schema locally for this component only.

• Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

Reject schema and Edit schema

• A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.

• Built-In: You create and store the schema locally for this component only.

• Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

Date columns

Add a row for each input column to standardize. The supported input data types are: Long, String, Date.

For each configured column, the component keeps the original input column and adds a Date column to the Main flow. The new column is named <COLUMNNAME>_STANDARDIZED.

Column

Select the incoming schema column to standardize. This field is required.

Output pattern

Specify the output pattern for the standardized value using java.time.format.DateTimeFormatter syntax. The default is yyyy-MM-dd'T'HH:mm:ss.

Fact column

When the input schema is dynamic, define the input column name.

The new column is named <DEFINEDCOLUMNNAME>_STANDARDIZED.

tStatCatcher Statistics

Select this checkbox to gather the Job processing metadata at the Job level as well as at each component level.

Use built-in patterns to parse source data

When selected, the component tries built-in date patterns in a fixed, deterministic order before any patterns in Additional patterns.

This option is selected by default.

Additional patterns

Add patterns for input date values that the built-in patterns do not parse. The component can also use these patterns when built-in patterns are not selected.

Specify the language and region that the component uses to interpret text in input date values, such as month names, day-of-week names, and AM/PM markers. For example, with the additional pattern "dd MMMM yyyy", the locale en parses 27 October 2026, while fr parses 27 octobre 2026. The values that the component cannot parse are sent to the Reject flow.

You can select one locale per Job.

By default, the parsing locale is en.

Time zone

Specify the time zone used when parsing input date and timestamp values.

By default, the time zone is UTC.

Invalid date handling

Send unparseable data to reject flow

When selected, the component sends unparseable input data to the Reject flow. When cleared, it keeps the data in the Main flow.

This option is selected by default.

Die on error

Select the checkbox to stop the execution of the Job when an error occurs.

Clear the checkbox to skip any rows on error and complete the process for error-free rows.

When errors are skipped, you can collect the rows on error using a Row > Reject connection.

Connections

The component provides Row connections named Main and Reject. The Reject schema includes a REJECT_REASON column for the error message.