Learning through curiosity and sharing
A few thoughts on learning by sharing, curiosity driven learning and working on what matters to you.
The fastest way to learn in data engineering is to solve real problems and share what you learn. Here are some ideas that crossed my mind this week on learning.
TL;DR
Sharing ideas online boosts learning and brings feedback.
Learning by solving real problems is more effective than tutorials.
Just-in-time learning through curiosity is more practical than just-in-case learning.
Working on unexpected challenges builds intuition over time.
Learning by sharing
When I started sharing my work online, it wasn’t from a motivation to teach others. It was more so I could learn faster. I often hear from people that they are hesitant to share their work because they don’t feel qualified yet. I felt that imposter syndrome too. But now, I wish I had started engaging on social media like LinkedIn sooner.
Putting my thoughts out there has helped me get immediate feedback from people who are more knowledgeable than me. It has refined how I learn and has given me more confidence in my work.
Impact on my career
I didn’t have a clear path into data engineering. Curiosity about pulling my bank transactions via an API got me hooked when someone commented on a post of mine about that possibility.
Each problem I encountered required new skills that I explored. Real-world challenges like these, which impacted my personal finances, shaped my learning more than any structured course.
Being driven by curiosity, I felt that the best way to learn wasn’t by following a predefined path but by solving real problems as they appeared.
Curiosity and learning what matters
While curiosity is a great driver for learning, it can have its limits. It’s easy to get lost in learning things that don’t really stick. There’s too much to know in data engineering, and learning everything just in case isn’t practical.
For me, the most effective approach is to let real problems dictate what I should learn. Just-in-time learning is far more valuable than trying to prepare for every possible data engineering task I may come across in personal projects or professional work.
Learning in data engineering isn’t a linear path. Some concepts might click immediately, while others take longer than expected. Tutorials gave the illusion of predictability at first, but unknowns always appear.
At the end of the day, recognising patterns in problems helped me learn far more than passive study. The more problems I solved, the faster I built intuition, making future challenges easier to tackle.
Thanks for reading,
Elias