I am super happy to see the growth in Python, fueled by AI and Data Engineering. I came to Python through Django — moving on from systems programming in C++. Django clicked instantly, I think I always enjoyed making applications more that the nitty gritty of core systems. But I also love Python/Django for their fast POCs, smooth dev experience, and the nature of interpreted languages. (And gosh, C’s .h files. I don’t want to deal with separate header files!)
As I see it, Python’s usage broadly comes down to two major applications:
- Web backends and
- data processing, AI, ML, LLMs
For a long time, I had a very elementary understanding of Python’s use in data science. Luckily, thanks to my new job role, I now do.
One thing that stands out to me is that the two worlds share the same language, but in different ways:
Python code in backend projects tends to be more structured. I’ve seen better scaffolding, use of MVC, stronger emphasis on OOP, and stricter typing. Production backends bring a different architectural discipline because deployment and uptime force it.
In contrast, the POCs on data/ML side often seem to start from ad-hoc scripts, notebooks, and pandas hacks. Great for exploration, but at the cost of maintainability.
It’s fun to bring the backend mindset into the mix on data engineering projects. Knowing how systems are deployed, how services are structured, and how things run in production (Kubernetes/AWS et al) makes it easier to think ahead about the workflows. It feels good to have finally connected the dots between the backend and data world. There’s a lot of value in that overlap than many people realize.
Stay tuned for some upcoming posts and projects if you’re excited about Python, AI, and Data Science.
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