Handling Variable Date Formats in FME: A Comprehensive Guide

Working with data from multiple sources often presents challenges, especially when dealing with date formats. This article addresses a common problem encountered when using FME (Feature Manipulation Engine) to process data from Excel files with inconsistent date formats and provides tested solutions for converting them into a uniform format.

The Challenge: Inconsistent Date Formats in Excel

A common issue arises when importing data from multiple Excel files. One attribute intended to represent a date might appear in different formats, such as "01.02.2010" (DD.MM.YYYY) in one file and "20100304" (YYYYMMDD) in another. This inconsistency can disrupt data processing, especially when subsequent transformations rely on a specific date format.

Understanding the Problem

Excel's flexibility can be a double-edged sword. While it allows users to format cells in various ways, this freedom can lead to inconsistencies when data is compiled from multiple sources. It's crucial to recognize that what Excel displays might not be the actual underlying data format. Therefore, it's essential to verify the actual data within FME using the FME Data Inspector.

Solution 1: Conditional Date Conversion with the DateTimeConverter

One approach is to use the DateTimeConverter transformer in conjunction with a Tester transformer. This method involves:

1. Identifying Date Formats: Use the Tester to identify the date format. For example, test if the attribute contains a period (".") to distinguish between "DD.MM.YYYY" and "YYYYMMDD" formats.
2. Conditional Conversion: Route the data to different DateTimeConverter transformers based on the identified format. Configure each DateTimeConverter with the appropriate input and output formats.
   • For "DD.MM.YYYY", set the input format to %d.%m.%Y and the output format to %Y%m%d.
   • For "YYYYMMDD", set the input format to %Y%m%d and the output format to %Y%m%d (no conversion needed).
3. Combining Results: After conversion, use a FeatureMerger or similar transformer to combine the data streams into a single output.

Pros:

• Simple and straightforward for a small number of known formats.
• Easy to understand and implement.

Cons:

• Not scalable if there are many different date formats.
• Requires manual configuration for each format.

Solution 2: Dynamic Date Parsing with AttributeCreator and DateTimeParse

A more robust solution involves using the AttributeCreator transformer with the @DateTimeParse function. This method allows FME to attempt to automatically detect and parse various date formats.

1. Attempt Auto-Detection: Use the following formula in the AttributeCreator: @DateTimeParse(@Value(DateAttributeName), FME|ISO, repair). This attempts to parse the date using recognized FME and ISO date/time formats. The repair option attempts to correct minor formatting issues.
2. Handle NULL Values: Check for NULL values resulting from failed parsing attempts using Conditional Values. This indicates that the date format was not recognized.
3. Iterative Parsing: For each unrecognized format, add a new line in the AttributeCreator with a different variation of the @DateTimeParse function, explicitly specifying the format. For example: @DateTimeParse(@Value(DateAttributeName), %d.%m.%Y, repair).
4. Prioritize Data Standardization: Work to enforce data standards at the source to minimize the variety of incoming date formats.

Pros:

• More flexible and can handle a wider range of formats.
• Reduces the need for manual configuration for each format.
• Leverages FME's built-in date parsing capabilities.

Cons:

• Auto-detection can be unreliable, especially when differentiating between month-first and day-first formats.
• Requires careful consideration of potential date formats.

AttributeCreator:
    New Attribute: ParsedDate
    Attribute Value: @DateTimeParse(@Value(OriginalDate), FME|ISO, repair)
    Conditional Value:
        Condition: @IsNull(@Value(ParsedDate))
        Attribute Value: @DateTimeParse(@Value(OriginalDate), %d.%m.%Y, repair)
    Conditional Value:
        Condition: @IsNull(@Value(ParsedDate))
        Attribute Value: @DateTimeParse(@Value(OriginalDate), %Y%m%d, repair)

Key Considerations

• FME Data Inspector: Always use the FME Data Inspector to verify the actual data format before attempting any transformations.
• Date/Time Formats: Familiarize yourself with the accepted FME and ISO date/time formats. Refer to the FME documentation for a complete list. (External Link)
• Data Quality: Address data quality issues at the source whenever possible. Enforcing data standards for date formats can significantly simplify data processing.
• Month-First vs. Day-First Ambiguity: Be aware of the potential for ambiguity when auto-detecting formats, especially when differentiating between month-first and day-first formats.

Conclusion

Handling variable date formats in FME requires a combination of understanding the data, leveraging FME's transformation capabilities, and implementing robust error handling. By using the techniques described in this article, you can effectively convert inconsistent date formats into a uniform standard, ensuring accurate and reliable data processing. Remember to prioritize data standardization at the source to minimize future challenges.

This article focuses on using the DateTimeConverter and AttributeCreator transformers. For additional information on data transformation, see our article on [Data Transformation Techniques in FME](Internal Link to related article on data transformation).