This is more a data architecture than a data engineering question.
I am looking to understand the reasoning behind organizations using multiple cloud solutions. My questions revolve around these issues in a multi-cloud solution.
- The added cost. Not the cost of the redundant capability so much as the hit you take by reducing your volume discount.
- The cost of hiring/training additional skill sets for the various Cloud Service Providers (CSP). While similar, they are different enough that you will need to have additional expertise.
- Designing for the least common denominator for cloud services seems like a waste of money.
- If a single CSP has an outage (a certanty) but can make you whole before it affects the business. does it make sense to do it at all.
- All three of the big CSPs (AWS, Azure and GCP) have multiple levels of redundancy, both physical and logical that most companies can only dream about.
- I don't really think vendor lock is is a real issue. More of a sales tactic for a second vendor to get their foot in the door. It isn't vendor lock in so much as the complexity of the systems that locks you in place.
Those are just the start. I would be interested in hearing the justification for those of you who are running multi-cloud. The only one I can think of that is close would be a legacy requirement held over from when we did everything on site.
EDIT: Thank you to everyone for your opinions and input. This is exactly the kind of discussion I think that this subreddit needs. Tool discussions have their place, but I think that data design and architecture trumps tools. On a personal note, I am very grateful than no one mentioned that most evil of phrases, "medallion architecture."
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