Etleap does exactly that: ETL data from Salesforce, Facebook, NetSuite, S3, Marketo, MySQL, and many others sources into a Redshift data warehouse.
A common use case we hear at Etleap (I’m on the team) is wishing to give analysts the ability to connect and manage the data pipelines they need to get more done in Looker. Otherwise they would need to depend on the engineering team to set up and maintain these pipelines, which can take weeks and break often. Meanwhile, engineering are happy to free their time from creating and maintaining these pipelines.
Since you can do transformations within Looker, for some companies it’s enough to have a data pipeline service that just ingests data and loads it into the warehouse (that is, ETL without the T, or ELT), such as Segment, Stitch, and Fivetran.
ask questions transform later.
However, as complexity of data goes up, the need for transformations outside the warehouse (and Looker) does as well.
What’s more, if your company has strict security requirements around data—think health, financial services, enterprise software providers, etc—loading everything sight-unseen into a warehouse is a non-starter. For cases like that, being able to transform data before it hits the warehouse (and Looker) can be the difference between passing and failing a security review.
All that to say...
It depends on the specific use case and requirements.
Hopefully this helps you think through that and points you to a few good options.