Want to convert data from Redshift to SQL Server ?
Try Withdata DBCopier, a Redshift to SQL Server converter for Windows, Mac, and Linux, Convert Redshift to SQL Server, Easy and Fast.
- Can run in GUI mode, Step by Step, just a few mouse clicks.
- Can run in Command line, for Scheduled Tasks and Streams.
- Support Windows, Linux, and macOS.
Convert data from Redshift to SQL Server
Login to Redshift and SQL Server -> Select source Redshift tables -> Set target SQL Server tables -> Convert Redshift to SQL Server
Choose source Redshift database and logon.
Choose target SQL Server database and logon.
Click “Wizard – Import All Tables” at task dialog.
then show the wizard.
1. Open source Redshift.
2. List target SQL Server tables.
3. Select SQL Server tables to import.
4. Convert data from Redshift to SQL Server.
More about Redshift to SQL Server converter
Download Withdata DBCopier
See also:
Redshift → SQL Server reference
Type mapping
| Value kind | in Redshift | in SQL Server |
|---|---|---|
| Int | integer | int |
| Decimal | numeric(p,s) | decimal(p,s) |
| Float | double precision | float |
| Text | varchar(n) | nvarchar(n) |
| Long text | varchar(max) | nvarchar(max) |
| Datetime | timestamp | datetime2 |
| Binary | (none) | varbinary(max) |
| Boolean | boolean | bit |
| JSON | super | nvarchar(max) |
Dialect differences
Both Redshift and SQL Server are database servers, so the differences below are dialect-level: the same concept is spelled differently on each side.
| Property | in Redshift | in SQL Server |
|---|---|---|
| port | 5439 | 1433 |
| identifiers | "double quotes" | [square brackets] |
| row limit | LIMIT n | SELECT TOP n |
| dates | timestamp, date | datetime, datetime2, date, time |
| long text | VARCHAR(MAX) | nvarchar(max) |
| binary | — | varbinary(max) |
Command line
| Side | Command |
|---|---|
| Redshift | UNLOAD to S3 |
| SQL Server | bcp |
| SQL Server (load) | bcp, or BULK INSERT |
| Redshift (load) | COPY from S3 |
No counterpart
- SQL Server has no equivalent for Binary – it has to be flattened or dropped.
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