# What’s Next[¶](https://knowledge.dataiku.com/latest/courses/dss-and-aws/dss-and-redshift/whatsnext.html#what-s-next "Permalink to this headline")

You can for instance easily create Charts on the Redshift dataset. Because Redshift can be used interactively thanks to its performance, Charts can be switched to “Live processing” (i.e Redshift will do most of the aggregation work):

Because live processing is enabled, Charts will be based on the entire dataset:

You may also want to analyze interactively the dataset with your own SQL queries, using a SQL notebook:

And finally, you could also write a SQL recipe in a larger workflow, for instance if you were to use Redshift to aggregate and reduce the size the base data, before passing it to R or Python for deeper analysis, or create a Model from it:

We’re done. A few steps will take your data into Redshift, and you’ll be good for large scale analysis in a matter of minutes.
