Hi @dhruvs2  😀.

A Lakeflow Job consists of tasks. The tasks can be things like notebooks or other jobs. If you want to orchestrate many jobs, I'd agree that having a job to do this is your best bet 😀. Then you can setup the dependencies as you require.

If you get stuck with anything, give me a shout 🙂. 

Once you've got the hang of how the jobs work, you can then look into parameterisation where you can start making things really dynamic! https://docs.databricks.com/aws/en/jobs/job-parameters

Don't forget about monitoring/observability either: https://docs.databricks.com/aws/en/jobs/monitor#view-jobs-and-pipelines 

In terms of compute for running the jobs. I'd say that Serverless is your best bet. If not, and you're using classic compute, it's recommended to use job compute. Here's a good article to read more about compute considerations: https://docs.databricks.com/aws/en/jobs/compute#what-is-the-recommended-compute-for-each-task 

All the best,
BS