Mastering Date and Timestamp Conversions in Azure Data Factory Data Flows
Azure Data Factory (ADF) data flows offer powerful transformations for manipulating data. One common requirement is converting dates and timestamps between different formats. This article provides a comprehensive guide to date and timestamp conversions within ADF data flows using expressions.
Scenario: Imagine you're working with a dataset containing dates stored in a "YYYY-MM-DD" format. You need to transform this data into a "MM/DD/YYYY" format for downstream systems.
Original Code:
{
"name": "ConvertDate",
"type": "DerivedColumn",
"description": "Convert date from YYYY-MM-DD to MM/DD/YYYY",
"source": {
"type": "DatasetReference",
"referenceName": "SourceDataset"
},
"sink": {
"type": "DatasetReference",
"referenceName": "TargetDataset"
},
"mapping": [
{
"source": {
"name": "DateColumn",
"type": "Column"
},
"sink": {
"name": "FormattedDate",
"type": "Column"
},
"expression": "toString(DateColumn, 'MM/dd/yyyy')"
}
]
}
Understanding the Problem:
The above code uses the toString function, which allows converting a date value into a string based on a specified format. However, this function might not always be sufficient when handling various date and time scenarios. We might need more complex transformations involving:
- Timestamp conversion: Converting timestamps from one timezone to another or extracting specific time components.
- Adding or subtracting intervals: Manipulating dates by adding or subtracting days, months, or years.
- Handling null values: Providing default values when encountering missing dates or timestamps.
Beyond the Basics: Deeper Insights into Date and Timestamp Conversions
-
Utilizing Date and Time Functions: ADF offers a wide array of functions for date and timestamp manipulation.
toTimestamp(Date, TimeZone): Converts a date string or column into a timestamp with the specified timezone.toDate(Timestamp, TimeZone): Extracts the date part from a timestamp.addDays(Date, Number): Adds the specified number of days to a date.formatDateTime(Timestamp, String): Formats a timestamp based on the provided format string.
-
Handling Time Zones: When dealing with timestamps from different regions, timezone conversion is crucial. ADF provides
toTimestampandtoDatefunctions with optional timezone parameters to handle this. -
Addressing Null Values: For scenarios where dates or timestamps might be missing, use the
coalescefunction to provide a default value. -
Customizing Format Strings: Format strings like 'MM/dd/yyyy' provide fine-grained control over the output format. Refer to the Azure Data Factory Expression Language documentation for a detailed list of supported format specifiers.
Example: Transforming Timestamps with Timezone Conversion
{
"name": "TransformTimestamp",
"type": "DerivedColumn",
"description": "Convert timestamp from UTC to EST",
"source": {
"type": "DatasetReference",
"referenceName": "SourceDataset"
},
"sink": {
"type": "DatasetReference",
"referenceName": "TargetDataset"
},
"mapping": [
{
"source": {
"name": "TimestampColumn",
"type": "Column"
},
"sink": {
"name": "ESTTimestamp",
"type": "Column"
},
"expression": "toTimestamp(TimestampColumn, 'UTC') as timestamp('EST')"
}
]
}
This example converts a timestamp in UTC to EST.
Conclusion:
Mastering date and timestamp conversions in Azure Data Factory data flows is vital for data transformation and analysis. By understanding the available functions, format strings, and timezone handling capabilities, you can easily adapt data to your specific requirements. For further exploration, consult the official Azure Data Factory documentation and explore the extensive examples available online.
