Raku excels at slicing and dicing input data. So, if you need to read from a database or a csv or anything similar, Raku is amazing at taking that data and converting it into the format to do processing on.
Not only that, but when Perl 5 was at the end of its life, comparing its libraries to both Python and Ruby, you'd often find the best library was for Perl.
Raku should be far faster than Python and Ruby and may in the future have better libraries with excellent concurrency support, making it a good choice to do processing over the data, not just converting it to the format you need. It has an easy interface with C and C++, so you can import libraries from there too, if you need more speed.
Before Python came to fame Perl was primarily used by two groups: 1) sys admins who needed bash style 1 liners, but in a cleaner language (This is where the dirty code stereotype comes from.) and 2) Web devs. Much of the early pre PHP internet and even post PHP internet was written in Perl. When Perl on hiatus the web world turned into this mess of frameworks like it is today. It used to be a more unified world.
And then there is the rare 3rd group. I worked on earlier ML projects in Perl before Python was a thing. I enjoyed Perl more than Python and so did the people around me. Moving to Python / R was somewhat painful knowing it could have been better. I hope Raku takes up this torch and is good as it appears to be.
Perl 5 has never been at the end of its life (though it was intended to be 15 years ago, things changed), this is one part of what this rename is meant to clarify. It still has a major release each year and modules are released to CPAN constantly: https://metacpan.org/recent
A lot of my colleagues in NLP and text processing still prefer to write in Perl. Granted they are mostly the old guard but it is prevalent in academic language technology research.
.. in the old guard of language technology research I guess, because in my NLP lab, no one knows the first thing about Perl (everything is done in Python)
More than that. I wrote my first machine learning project in Perl.
Perl 5 is better than Python in many ways, but worse in other ways. Raku seems to have none of the downsides Perl 5 had, but all of the upsides. Raku looks like it could be a valid replacement for Python in the ML space.
Also, back then, Perl 5 had better libraries than Python. Though, that was quite a while ago.
There are many more tasks in NLP than what SpaCy offers even though it is a nice API. I use SpaCy near daily, but academic research goes beyond NER, dep parsing and PoS tagging.
For instance, [1] is a tool for text-to-pictograph translation system I worked on. The core engine is entirely written in Perl 5.
Years ago I worked with Rogue Wave Software, which sold C++ libraries. There was a customer who wanted to sort a huge dataset, and RW's sort code took something like 15 minutes to sort. Perl took 15 seconds. I don't know if it was ever figured out why, but it was (is) just one of those signs of how Perl still can be amazing at some things.
Still have my O'Reilly 1st Annual Perl Conference bag around. Good times.
we are using it internally for a lot of management apps. we just deployed a new authorization frontend for graphite / carbon using cro that is handling several million daily requests (per small EC2 instance).
raku is a great language to work with for us. it is feature rich, has good abstractions, and developer friendly. if you like the syntax then give it a try.
I like raku's grammars, and I appreciate the idea of having them as first class, in a way other languages don't have them.
But you have parser libraries that work very well in all major languages, and I'm not sure being integrated in the language makes Raju more valid for any project.
What it does, imo, is offer a special way to solve some stuff by already being there. But it's like saying someone would choose python for a project because it has a built-in set data type.
