This is why data warehousing exists. That way you can use many different BI tools against a consistent set of data and pre-calculate a lot of commonly-used summary data.

ETL also isn't something you can just do automagically. It requires an understanding of the data and your goals, because you essentially have to build both your data model and your reporting requirements into your ETL process. You could probably do it automagically for some simple cases, but for most real-world scenarios it's just going to be easier to write a Python/Perl script to run your ETL for you.

BI / reporting requires a lot of plumbing to work correctly. You have to set up read-only clones of your DBs for reporting (because you don't want to be running large queries against production servers) and generally an ETL process that dumps everything into a data warehouse. From there, you can push subsets of that data out to various BI tools that provide the interface.