Yes, it is still a great glue for large projects. Right now I'm building a custom public health database with data from many different sources - some of them P

Yes, it is still a great glue for large projects. Right now I'm building a custom public health database with data from many different sources - some of them PDFs and other files with really irregular formatting that require single-use scripts with complex regular expressions. Perl was my first language so I can always purr through those problems, and I'm really comfortable (happy even) debugging a Perl script. I'm also maintaining a legacy bioinformatics project with about 30,000 lines of Perl in it and still making expansions on it. I won't defend Perl as the best choice for that project other than to say I was around some very good Perl hackers / sysadmin types and I picked up the only tool with a knowledge well nearby. It did however make me a much better programmer, especially since I ended up implementing a lot of functions manually that I probably should have just found on CPAN or used as a built-in method in another language. Perl is also really great when you are manually splitting data and jobs across a few thousand nodes and locally tweaking the run parameters for each job. Now that I've learned a few more languages I know that sort of thing might be better done with Hadoop but for a large GPFS system I rarely had problems. I will say that for memory considerations (the main ceiling I bumped into on the old cluster) object-oriented Perl sucked and was very slow. If you wanted to roll your own pseudo-objects with well designed nested hashes though you could really wail.