Data type changes for Snowflake in ELT & CDC

Please refer to this page to view the data type mappings from your source to Snowflake. If the data type mapping differs on the Snowflake side after performing the change on the source, the following section explains how these changes will be handled by the automatic replication feature.

Original Data Type	Updated Data Type	Data Type Conversion	Column properties adjusted	Backfill supported
NUMBER	FLOAT	FLOAT	❌	✅
	NUMBER	NUMBER	✅	N/A
	TEXT	TEXT	❌	✅
	VARIANT	VARIANT	❌	✅
	Others	Same as Updated Data Type	❌	❌
FLOAT	NUMBER	NUMBER	❌	✅
	TEXT	TEXT	❌	✅
	VARIANT	VARIANT	❌	✅
	Others	Same as Updated Data Type	❌	❌
TEXT	NUMBER	NUMBER	❌	✅
	FLOAT	FLOAT	❌	✅
	TEXT	TEXT	✅	N/A
	DATE	DATE	❌	✅
	TIMESTAMP_TZ	TIMESTAMP_TZ	❌	✅
	TIMESTAMP_NTZ	TIMESTAMP_NTZ	❌	✅
	TIME	TIME	❌	✅
	Others	Same as Updated Data Type	❌	❌
VARIANT	TEXT	TEXT	❌	✅
	Others	Same as Updated Data Type	❌	❌
DATE	TEXT	TEXT	❌	✅
	TIMESTAMP_TZ	TIMESTAMP_TZ	❌	✅
	TIMESTAMP_NTZ	TIMESTAMP_NTZ	❌	✅
	Others	Same as Updated Data Type	❌	❌
TIMESTAMP_TZ	TEXT	TEXT	❌	✅
	DATE	DATE	❌	✅
	TIMESTAMP_NTZ	TIMESTAMP_NTZ	❌	✅
	TIME	TIME	❌	✅
	Others	Same as Updated Data Type	❌	❌
TIMESTAMP_NTZ	TEXT	TEXT	❌	✅
	DATE	DATE	❌	✅
	TIMESTAMP_NTZ	TIMESTAMP_TZ	❌	✅
	TIME	TIME	❌	✅
	Others	Same as Updated Data Type	❌	❌
TIME	TEXT	TEXT	❌	✅
	Others	Same as Updated Data Type	❌	❌
BINARY	Any	Same as Updated Data Type	❌	❌

Important Notes

Narrowing Changes: The pipeline will ignore narrowing changes in numeric precision and scale, or string length to ensure compatibility between old and new data in the destination.
Numeric Field Adjustments: When switching between FLOAT and NUMBER, be aware that precision may increase or decrease due to the differing precision and scale characteristics of these types.
Handling Precision and Scale Changes: Changes in precision and scale can only be applied to the NUMBER data type. If the source schema maps to a data type different from NUMBER, the change will not be reflected in the destination.
Backfilling: Backfilling a column involves copying existing data from the old column to a new column with the updated data type, ensuring no data is lost during the conversion. Please note that we leverage Snowflake’s data type coercion to backfill and transform data from the original data type to the new one. If Snowflake is unable to transform the values to the desired data type during backfilling, the pipeline will fail, prompting a resync of the affected table.

Last modified on May 12, 2026

Data type mapping for Snowflake in ELT & CDC

Data type mapping for BigQuery in ELT & CDC

⌘I

Getting Started

Pipelines

Sources

Destinations

Monitoring

Security

Transforms

Data Observability

Data type changes for Snowflake in ELT & CDC

Important Notes

​Important Notes

Important Notes