Comment by hodgesrm
9 days ago
> * Data portability guarantees: Vendors must spell out how data gets migrated out, and all formats must be either open or (at minimum) fully documented.
This is not practical for data of any size. Prod migrations to a new database take months or even years if you want things to go smoothly. In a crisis you can do it in weeks but it can be really ugly, That applies even when moving between the same version of open source database, because there's a lot of variation between the cloud services themselves.
The best solution is to have the data in your own environment to begin with and just unplug. It's possible with bring-your-own-cloud management combined with open source.
My company operates a BYOC data product which means I have an economic interest in this approach. On the other hand I've seen it work, so I know it's possible.
I'd love to know more about BYOC. Does that apply to the raw data (e.g., the database lives inside the enterprise) or the entire application stack (e.g., the enterprise is effectively self-hosting the cloud).
It seems like you'd need the latter to truly be immune to cloud-vendor problems. [But I may not understand how it works.]
Here's how we do it for analytic systems: data and software. The software services are open source running on Kubernetes. If you don't like the vendor or the vendor goes away, the existing services keep running. You can also maintain them because the stack is open source.
This is different from what the local-first article is describing, which addresses data for individuals. That's a much harder problem to solve at scale.