# Load your data to Redshift[¶](https://knowledge.dataiku.com/latest/courses/dss-and-aws/dss-and-redshift/load-to-redshift.html#load-your-data-to-redshift "Permalink to this headline")

Loading your dataset into Redshift will now be very easy. Just go back to the Flow screen again, and create a new “Sync” recipe from the “github\_s3” dataset:

This will create a new “github\_t” dataset, stored in your Redshift connection.

The summary screen showing up will let you see again the input / output of your Sync recipe (note that again the partitioning scheme is copied), and build (i.e copy your S3 data to Redshift) your dataset directly from there:

Hit the “Run” button and wait for the job to complete:

A little bit less than 2 minutes to wait, and your data is in Redshift! And note that the resulting table is partitioned, and we didn’t even have to care about its schema.
