For example, R (a domain-specific language for stats) may be a better language for doing heavy duty statistics, and we’ve been told many times that it’s easier to learn if you think like a statistician.
A successor to Python might require less code (and be a “lower code” language, if that’s meaningful) it would almost certainly have to do something better. That position between big code and minimal code probably has a lot to do with its success. It’s not great for large programs (though it has certainly been used for them) its sweet spot is programs that are a few hundred lines long. (Perl is legendary for inscrutable one-liners.) Python is in the middle. A language like bash or Perl is designed for short programs that connect other utilities bash and Perl scripts typically have a single author, and are frequently only a few lines long. These are projects that can take years to develop, and run to millions of lines of code. Languages like Java and C++ are intended for large projects involving collaboration between teams of programmers. In taking this bigger-picture, language-based approach to understanding low-code, we also have to take into account what the low-code language is being used for.
(Although, in an ironic and unfortunate twist, many of the people who spent their careers plugging in patch cords, toggling in binary, and doing math on mechanical calculators were women, who were later forced out of the industry as those jobs became “professional.” Democratization is relative.) It may be surprising to say that Python is a low-code language, but it takes less work to accomplish something in Python than in C rather than building everything from scratch, you’re relying on millions of lines of code in the Python runtime environment and its libraries.
It’s a history of democratization and reducing barriers to entry.
In this sense, the history of programming is the history of low-code. Python is low-code relative to C++ C and FORTRAN are low-code relative to assembler assembler is low-code relative to machine language and toggling switches to insert binary instructions directly into the computer’s memory. So we could think about low-code as tools similar to Excel, tools that enable people to use computers effectively without learning a formal programming language.Īnother way of looking at low-code is to take an even bigger step back, and look at the history of programming from the start. And spreadsheets have enabled a whole generation of businesspeople to use computers effectively-most of whom have never used any other programming language, and wouldn’t have wanted to learn a more “formal” programming language. It’s a different, non-procedural, non-algorithmic approach to doing computation that has been wildly successful: is there anyone in finance who can’t use Excel? Excel has become table stakes. One way of looking at low-code starts with the spreadsheet, which has a pre-history that goes back to the 1960s-and, if we consider paper, even earlier. In that context, low-code quickly becomes a complex topic.
Low-code: what does it even mean? “Low-code” sounds simple: less is more, right? But we’re not talking about modern architecture we’re talking about telling a computer how to achieve some result.
Get a free trial today and find answers on the fly, or master something new and useful. Join the O'Reilly online learning platform.
It will be fundamental for anyone working in software development-and, indeed, anyone working in any business that is poised to become a digital business-to understand what low-code means, how it will transform their roles, what kinds of issues it creates, why it won’t work for everything, and what new kinds of programmers and programming will emerge as a result.
This report is for programmers and software development teams looking to navigate that shift and understand how low-code and no-code solutions will shape their approach to code and coding. Programmers know their jobs won’t disappear with a broadscale low-code takeover (even low-code is built on code), but undeniably their roles as programmers will shift as more companies adopt low-code solutions. As more companies look to integrate low-code and no-code solutions into their digital transformation plan, the question emerges again and again: what will happen to programming? In the past decade, the growth in low-code and no-code solutions-promising that anyone can create simple computer programs using templates-has become a multi-billion dollar industry that touches everything from data and business analytics to application building and automation.