How to back up in the cloud

Rescue Approach

Conclusions

Backups in clouds are different from those in conventional setups. From the vendor's perspective, automation plays a major role. If automation can be restored quickly in the form of a boot infrastructure, bootstrapping the new platform is also fast, and only specific configuration files of the individual cloud services and their metadata need to be included in the backup. Everything else is generic and can be reproduced through automation when needed – and as often as desired.

On the other hand, the cloud is more uncomfortable for customers. Whereas in the past, the provider would take care of backups, now do-it-yourself is the order of the day – unless you make a special agreement with the provider specifically for this purpose. Large providers of public clouds in particular usually do not offer any products for backups, so customers are ultimately dependent on their own initiative.

For large parts of a setup, the same applies as for the underlay: Automation, orchestration, and CI/CD are the keys to success. In the end, only the genuinely persistent data of a virtual environment should be backed up (e.g., customer data from a database).

All virtual instances of the setup, infrastructure, and software required in the environment must be reproducible at all times. If you take this advice to heart, you will usually have very small backup volumes, without spoiling your fun in the cloud.

Clearly, backup habits from the school of "back up everything" have no place in the cloud. Because backups of whole VMs or complete containers eat up a large amount of disk space for repeatedly backing up the same data and because backups of this kind cannot be restored faster than backups made and restored with cloud tools, it is simply pointless to back up everything.

If you consistently use cloud tools, however, you will also make life easier for yourself in other situations. Database updates are far easier if the database and data records are separated from each other, making it easy to create a new DB instance with updated software, into which you then only insert the existing dataset – all done.

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