Using Python for processing of large datasets (convincing managment)

[Thomas Jensen]

Hello group (and list :-),

I've used Python for several years (and followed this group until about 
6 months ago).
I work in a small company which specialises in collecting and procesing 
financial data. Most of our production environment is based on Microsoft 
stuff like ASP/VBScript, VB6, WinNT, MS SQL Server, etc.

One of the next development tasks is rewriting the nightly processing 
job which is having problems with our ~100mb database (it it written in 
Borland C++, but absolutely not optimized for speed!).

The goals of the rewritten piece of software would be:
* Improved speed
* Improved scalability - parallel processing on multiple machines/CPUs
* Improved scalability - ability to handle greater databases (>1gb)
* Ability to calculate only a subset of the data

Now, instead of rewriting the job in C++, I'd (of course) like to use 
Python.
However the CEO (small company, told you :-), made a couple of somewhat 
valid points against it.
1) He was worried about getting a replacement devlopper in case I left.
2) He said, "Name 3 companies using Python for key functions"
3) He was worried about the stability/reliability of python in our 
production environment (you know, 99.999 % and all that)

I was hoping someone in this group could help with some really 
compelling arguments, as I'd really to use Python for this job.

Best regards
Thomas Jensen