AWS Redshift Data Processing
The 2 options for running ETL on Redshift

Create some "create table as" type SQL, which will take your source
tables as input and generate your target (transformed table)
Do the transformation outside of the database using an ETL tool. For
example EMR or Glue.

Generally, in an MPP environment such as Redshift, the best practice is to push the ETL to the powerful database (i.e. option 1). 
Only consider taking the ETL outside of Redshift (option 2) where SQL is not the ideal tool for the transformation, or the transformation is likely to take a huge amount of compute resource.
There is no inbuilt scheduling or orchestration tool. Apache Airflow is a good option if you need something more full featured than cron jobs.
I'm working with a small company currently that stores all of their app data in an AWS Redshift cluster. I have been tasked with doing some data processing and machine learning on the data in that Redshift cluster.
The first task I need to do requires some basic transforming of existing data in that cluster into some new tables based on some fairly simple SQL logic. In an MSSQL environment, I would simply put all the logic into a parameterized stored procedure and schedule it via SQL Server Agent Jobs. However, sprocs don't appear to be a thing in Redshift. How would I go about creating a SQL job and scheduling it to run nightly (for example) in an AWS environment? 
The other task I have involves developing a machine learning model (in Python) and scoring records in that Redshift database. What's the best way to host my python logic and do the data processing if the plan is to pull data from that Redshift cluster, score it, and then insert it into a new table on the same cluster? It seems like I could spin up an EC2 instance, host my python scripts on there, do the processing on there as well, and schedule the scripts to run via cron?
I see tons of AWS (and non-AWS) products that look like they might be relevant (AWS Glue/Data Pipeline/EMR), but there's so many that I'm a little overwhelmed. Thanks in advance for the assistance!