# Amazon Redshift
> Move data from Amazon Redshift into MotherDuck by unloading Parquet to S3, or read Redshift Spectrum tables in place through the AWS Glue Data Catalog.
The path into MotherDuck goes through S3: Redshift writes the data out with `UNLOAD`, and MotherDuck reads the files. There is no DuckDB extension that attaches Redshift directly.

## Unload to S3 and read the files

In Redshift, unload the table or query result to your bucket as Parquet:

```sql
UNLOAD ('SELECT * FROM public.orders')
TO 's3://my-bucket/redshift-unload/orders/'
IAM_ROLE 'arn:aws:iam::<account_id>:role/<redshift_unload_role>'
FORMAT AS PARQUET
MAXFILESIZE 256 MB
ALLOWOVERWRITE;
```

The IAM role needs `s3:PutObject` on the destination prefix. Parquet keeps the column types, so prefer it over CSV.

In MotherDuck, store the bucket credentials in a secret and read the files:

```sql
CREATE SECRET my_s3_secret IN MOTHERDUCK (
    TYPE S3,
    KEY_ID '<aws_access_key_id>',
    SECRET '<aws_secret_access_key>',
    REGION '<aws_region>'
);

CREATE TABLE orders AS
SELECT * FROM read_parquet('s3://my-bucket/redshift-unload/orders/*.parquet');
```

For a whole schema, script the `UNLOAD` per table from `SVV_TABLES`, then load each prefix in MotherDuck. For repeat loads, unload only new rows and append with the watermark patterns in [Data loading patterns](/key-tasks/loading-data-into-motherduck/loading-patterns).

## Read Spectrum tables in place

If you query external tables with Redshift Spectrum, the data already sits in S3 and is registered in the AWS Glue Data Catalog. MotherDuck can read the same data without a copy:

- For Iceberg tables, attach the Glue Data Catalog as a MotherDuck database. See [AWS Glue in the Apache Iceberg page](/integrations/file-formats/apache-iceberg#aws-glue).
- For plain Parquet or CSV prefixes, read the files directly with `read_parquet` or `read_csv`, using `hive_partitioning` when the prefix encodes partition columns. See [S3 import best practices](/key-tasks/cloud-storage/s3-import-best-practices).

## Use an ingestion tool

Ingestion tools that list Redshift as a source can load into MotherDuck as a destination: [dlt](/integrations/ingestion/dlt), [Sling](/integrations/ingestion/sling), [Airbyte](/integrations/ingestion/airbyte), and [Fivetran](/integrations/ingestion/fivetran). Use one when Redshift is one source among several and you already run the tool.

## Things to know

- **Don't attach Redshift with the `postgres` extension.** Redshift speaks a Postgres-derived protocol, but DuckDB's [PostgreSQL extension](/key-tasks/loading-data-into-motherduck/loading-data-from-postgres) targets PostgreSQL and attaching Redshift is not a supported configuration. Route bulk reads through `UNLOAD` instead.
- **Distribution and sort keys don't carry over.** MotherDuck has no `DISTKEY` or `SORTKEY`. Physical layout is handled for you, so drop those definitions rather than translating them. If a query is slow, see [Query performance](/key-tasks/query-performance).
- **Type mapping.** Redshift `SUPER` unloads as JSON text; cast it to DuckDB `JSON` or a `STRUCT` after loading. Redshift `VARCHAR(max)` becomes a plain DuckDB `VARCHAR` with no length limit to declare.
- **Unload region.** Put the S3 bucket in the same region as your Redshift cluster to avoid cross-region transfer costs on the way out.

## Related content

- [Loading data from cloud storage or HTTPS](/key-tasks/loading-data-into-motherduck/loading-data-from-cloud-or-https)
- [S3 import best practices](/key-tasks/cloud-storage/s3-import-best-practices)
- [Amazon S3](/integrations/cloud-storage/amazon-s3)
- [Redshift `UNLOAD` documentation](https://docs.aws.amazon.com/redshift/latest/dg/r_UNLOAD.html)


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