How frequently are the database changing and how much downtime can your business allow?
There are a multitude of non-realtime ways to move a snapshot of the databases such as:
Full Database Backups
Scripting the Database (with Data)
3rd Party Synchronization Tools (RedGate's SQL Compare)
There's also a series of more realtime continuous synchronization options as well:
AlwaysOn Availability Groups
They each have their pros and cons, depending on your needs. If you need a one-time move and can schedule a maintenance window where the databases won't change before you cut-over, then the BACPAC file might be the simplest way to migrate into Azure. A tool like RedGate's SQL Compare is also very handy, especially if you think you'll need to move things more than once. If you need on-going synchronization to the cloud for a period of time before you can cut-over then AlwaysOn Availability Groups might be the simplest choice or Replication (Transactional or Snapshot depending on how frequently your data changes). But please read over each of them and decide on what best fits your use case.
Regarding your question in the comments of choosing a Data Lake vs using Synapse:
Neither of them are bound to specific file types, all of them can consume pretty much any file type, so think of the systems being file type agnostic.
The purpose of a Data Lake is to house varying structured data before it becomes structured and consumed elsewhere. E.g. you can have two Excel files with similar data but one is missing a few columns from the other and visa-versa, but both are easily digestible into the same Data Lake so that you can centralize the data into one place and eventually report off it. The Data Lake may be able to consume different data and file types more easily though.
Synapse is more structured like a database (or series of databases) so importing data into it requires a little bit different workflow. Since you're sticking to standardized data storage formats you should be fine to use Synapse to pull the data into it as well (so either system will work in terms of pulling the data in, Data Lakes are just more flexible in the varying structures of data it can house in one place). Synapse, being more structured than a Data Lake, means it's more ready to report off of upfront too. A Date Lake likely will require you to setup a series of process to transform and move the data around before you can report off it.
So it really comes down to how structured vs unstructured your data is, and how much ETL you want to develop into the entire workflow. But one system isn't necessarily better than the other, they just have slightly different purposes.
Data Lake for varying structures of data so more flexible for storing the data in one place, but more ETL work if needed to report off of, Synapse for more structured data so less flexible but more ready to report directly off of. My final thoughts are Data Lakes are good for data that's being consolidated from many sources to be reshipped to some consuming system, so basically just a centralized place of multiple different datasets as a staging ground before moved to its final consuming destination.