ELT/CDC: Monitoring - Data Type Changes - Redshift

Important Notes

If the data type mapping differs on the Redshift side, the following section explains how these changes will be handled by the automatic replication feature.

Original Data Type	Updated Data Type	Data Type Conversion	Widening / Narrowing	Column properties adjusted	Backfill supported
BIGINT	INTEGER	BIGINT	Narrowing	❌	N/A
	SMALLINT	BIGINT	Narrowing	❌	N/A
	NUMERIC	NUMERIC	Widening	✅	✅
	DOUBLE_PRECISION	NUMERIC	Widening	✅	✅
	REAL	NUMERIC	Widening	✅	✅
	VARCHAR	VARCHAR	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌
INTEGER	BIGINT	BIGINT	Widening	❌	✅
	SMALLINT	INTEGER	Narrowing	❌	N/A
	NUMERIC	NUMERIC	Widening	✅	✅
	DOUBLE_PRECISION	DOUBLE_PRECISION	Widening	❌	✅
	REAL	NUMERIC	Widening	✅	✅
	VARCHAR	VARCHAR	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌
SMALLINT	BIGINT	BIGINT	Widening	❌	✅
	INTEGER	INTEGER	Widening	❌	✅
	NUMERIC	NUMERIC	Widening	✅	✅
	DOUBLE_PRECISION	DOUBLE_PRECISION	Widening	❌	✅
	REAL	REAL	Widening	❌	✅
	VARCHAR	VARCHAR	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌
NUMERIC	BIGINT	NUMERIC	Widening	✅	✅
	INTEGER	NUMERIC	Widening	✅	✅
	SMALLINT	NUMERIC	Widening	✅	✅
	NUMERIC	NUMERIC	-	✅	N/A
	DOUBLE_PRECISION	NUMERIC	Widening	✅	✅
	REAL	NUMERIC	Widening	✅	✅
	VARCHAR	VARCHAR	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌
DOUBLE_PRECISION	BIGINT	NUMERIC	Widening	✅	✅
	INTEGER	DOUBLE_PRECISION	Narrowing	❌	N/A
	SMALLINT	DOUBLE_PRECISION	Narrowing	❌	N/A
	NUMERIC	NUMERIC	Widening	✅	✅
	REAL	DOUBLE_PRECISION	Narrowing	❌	N/A
	VARCHAR	VARCHAR	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌
REAL	BIGINT	NUMERIC	Widening	✅	✅
	INTEGER	NUMERIC	Widening	✅	✅
	SMALLINT	REAL	Narrowing	❌	N/A
	NUMERIC	NUMERIC	Widening	✅	✅
	DOUBLE_PRECISION	DOUBLE_PRECISION	Narrowing	❌	N/A
	VARCHAR	VARCHAR	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌
VARCHAR	VARCHAR	VARCHAR	-	✅	N/A
	Others	Same as Updated Data Type	-	❌	❌
DATE	VARCHAR	VARCHAR	Widening	❌	✅
	TIMESTAMP_WITH_TIMEZONE	TIMESTAMP_WITH_TIMEZONE	Widening	❌	✅
	TIMESTAMP_WITHOUT_TIMEZONE	TIMESTAMP_WITHOUT_TIMEZONE	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌
TIMESTAMP_WITH_TIMEZONE	VARCHAR	VARCHAR	Widening	❌	✅
	DATE	TIMESTAMP_WITH_TIMEZONE	Narrowing	❌	N/A
	TIMESTAMP_WITHOUT TIMEZONE	TIMESTAMP_WITH_TIMEZONE	Narrowing	❌	N/A
	Others	Same as Updated Data Type	-	❌	❌
TIMESTAMP_WITHOUT TIMEZONE	VARCHAR	VARCHAR	Widening	❌	✅
	DATE	TIMESTAMP_WITHOUT TIMEZONE	Narrowing	❌	N/A
	TIMESTAMP_WITH TIMEZONE	TIMESTAMP_WITH TIMEZONE	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌
TIME_WITH TIMEZONE	VARCHAR	VARCHAR	Widening	❌	✅
	Others	Same as Updated Data Type	-	❌	❌

Important Notes

Narrowing Changes: The pipeline will ignore narrowing changes to ensure compatibility between old and new data in the destination.
Numeric Field Adjustments: In some cases, the pipeline will change the data type of a numeric field to NUMERIC to accommodate both old and new data in the destination.
Handling Precision and Scale Limits: With numeric data types, the pipeline will attempt to fit both old and new data into the destination. However, due to constraints on the destination’s maximum precision and scale, this may not always be possible. If the data cannot be accommodated, an error will be thrown, prompting a re-sync.
Backfilling: Backfilling a column involves copying existing data from the old column to a new column with the updated data type, ensuring that no data is lost during the conversion.

Last modified on April 15, 2026

ELT/CDC: Monitoring - Data Type Mapping - Redshift

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ELT/CDC: Monitoring - Data Type Changes - Redshift

Important Notes

Getting Started

Sources

Destinations

Monitoring

Security

Transforms

Data Observability

Documentation Index

​Important Notes

Important Notes