Most people consume content about data engineering. Very few build. 674 contributions in the last year. Consistent across October through March, no dead months, no gaps. Not because every day was productive. Because the habit of showing up compounds faster than any course ever will. If you're trying to break into data engineering, close the tutorial. Open a terminal. Build something broken, fix it, commit it, repeat. Your GitHub is either evidence or silence. Make it evidence. What's stopping you from committing something today? #DataEngineering #Python #GitHub
Number of commits and lines of code doesn't demonstrate how good a programmer someone is. With modern prompt engineering these prehistoric metrics need to die.
To be fair, you could just try running automated CI/CD burner jobs or "deployments" to make it look like you're doing something, just saying..not that I'd ever suggest anybody do that of course
The three repos visible here,Kafka Streaming Pipeline, Log Processing System, and API Processing System, are all public and fully documented with architecture decisions, design tradeoffs, and production considerations. Link below. 👇 https://github.com/Maxwell-Selassie