Red Black Tree versus B Tree

Question

I have a project in which I have to achieve fast search, insert and delete operations on data ranging from megabytes to terabytes. I had been studying data structures of late and analyzing them. Being specific, I want to introduce 3 cases and ask questions on that:

1. The data is much more than what the memory can handle (sample ranges in 10-15 terabytes) at one go. In this case, I would store the data structure on a disk.

2. The data is relatively less compared to the memory of the system and thus it can be stored and operated in the memory itself for speed.

3. The data is more than free memory and assume it is less than the size of a possible contiguous chunk of data in the paging file. thus I store the data structure in a file on disk and do a memory mapping of the file.

The conclusions I have drawn are:

For case 1, I should use a B-Tree for faster access as it saves on lag produced by disk rotation

For case 2, I should use a Red Black Tree for faster access as data is on memory and no. of elements needed to be scanned in worse case would be lesser than one i have to do if I use B Trees

For case 3, I am doubtful on this one, the page file is on disk uses native OS I/O to operate on files, so should B Tree be a better option or a Red Black tree?

I want to know where the above three conclusions go right and where it goes wrong and how I can improve on performance in the three separate cases.

I am using the C++ Language, with a red black tree and a B tree, both which I have designed from scratch. I am using Boost library for File mapping.

Update 1:: Was reading through this post in stackoverflow. Got some real good insights, which make me feel that the type of comparisons I have done in the cases may be faulty. A link was posted in the most-voted-for-answer http://idlebox.net/2007/stx-btree/stx-btree-0.8.3/doxygen-html/speedtest.html