If you effectively have a 1:1 between the source line and the fact, and you store a source system booking code in the fact (no dimensional modelling rules against that) then I suggest you have a two step load process.
delete/insert the last x days bookings based on reservation date (or whatever you consider to be the primary fact date),
delete/insert based on all source booking codes that have changed (you will of course have to know this beforehand)
You just need to consider what constraints this puts on future development, i.e. when you get additional source systems to add, you'll need to maintain the 1:1 fact to source line relationship to keep your load process consistent.
I've never updated a fact record in a dataload process, but always delete/insert a certain data domain (i.e. that domain might be trailing 20 days or source system booking code). This is effectively the same as an update but also takes cares of deletes.
With regards to auditing changes in the source, I suggest you write that to a different table altogether, not the main fact, as it's purpose will be audit, not analysis.
The requirement to identify changed records in the source (for data loads and auditing) implies you will need to create triggers and log tables in the source or enable native SQL Server CDC if possible.
At all costs avoid using the SSIS lookup component as it is totally ineffective and would certainly be unable to operate on 45 million rows.
Stick with the 'insert/delete a data portion' approach as it lends itself to SSIS ability to insert/delete (and its inability to efficiently update or lookup)
In answer to the follow up questions:
1:1 relationship in fact What I'm getting at is you have no visibility on any future systems that need to be integrated, or any visibility on what future upgrades to your existing source system might do. This 1:1 mapping introduces a design constraint (its not really a constraint, more a framework). Thinking about it, any new system does not need to follow this particular load design, as long as it's data arrive in the fact consistently. I think implementing this 1:1 design is a good idea, just trying to consider any downside.
If your source has a reliable modified date then you're in luck as you can do a differential load - only load changed records. I suggest you:
- Load all recently modified records (last 5 days?) into a staging table
- Do a DELETE/INSERT based on the record key. Do the delete inside SSIS in an execute SQL task, don't mess about with feeding data flows into row-by-row delete statements.
Audit table:
The simplest and most accurate way to do this is simply implement triggers and logs in the source system and keep it totally separate to your star schema.
If you do want this captured as part of your load process, I suggest you do a comparison between your staging table and the existing audit table and only write new audit rows, i.e. reservation X last modified date in the audit table is 2 Apr, but reservation X last modified date in the staging table is 4 Apr, so write this change as a new record to the audit table. Note that if you do a daily load, any changes in between won't be recorded, that's why I suggest triggers and logs in the source.
- DELETE/INSERT records in Fact
This is more about ensuring you have an overlapping window in your load process, so that if the process fails for a couple of days (as they always do), you have some contingency there, and it will seamlessly pick the process back up once it's working again. This is not so important in your case as you have a modified date to identify differential changes, but normally for example I would pick a transaction date and delete, say 7 trailing days. This means that my load process can be borken for 6 days, and if I fix it by the seventh day everything will reload properly without needing extra intervention to load the intermediate days.