Always write your analytics data to a separate DB in a periodically run job. Only store aggregated anonymized data in the analytics DB you expose to internal stakeholders via tools like Metabase.
Usually production is used for fetching and updating a small number of records at a time (think updating a shopping cart), and has strict latency requirements whereas analytics involves reading a large amount of data in columns (think count group by one or two columns), and can be done in batches where the results can get a more and more stale until the next batch runs.
Then you have your warehouse database that you updated once a day with information from prod.
Mostly in that the leaves of your system (parts that nothing else connects to or builds on) are generally a low risk place to try new things sometimes. If you do run into any intractable issues, it’s also an easy spot to pluck it off and replace it.
This should further emphasize the need to isolate these tools and ensure they are only accessible to people who need them.
I can't recommend any specific tools without knowing a lot about the environment, but if you're looking for terms to google: ELT (Extract, Load, Transform) and CDC (Change Data Capture) will give you a sense of the landscape.
edit: the sibling comment that mentions Airflow is a good answer for an example of an ELT workflow.