Extract. Transform. Read.

A newsletter from PipelineToDE​

Hi past, present or future data professional!

After choosing a dataset, one of the most significant decisions you must make when creating displayable work is: How am I going to build this thing?

For some, you may try to “vibe code” along with an LLM doing the grunt technical work. If you choose this approach, be warned: Nearly half of all “vibe code” generated contains security vulnerabilities and that’s before you even consider its ability to run. Others may immediately jump into an IDE, confident in their existing skills like Python.

These two examples illustrate a conflicting decision you must make from the outset: Are you optimizing for “showing off” strong, existing skills? Or do you want to signal to an employer you can learn and employ new skills on the fly?

This is the data practitioner’s Catch-22. Do you opt for a sure bet, a familiar tech stack, or do you take a risk and showcase an emerging or “hot” technology that might not be among your core strengths?

Perhaps the biggest determining factor for learning vs. showing off is your time constraint. How much time can you dedicate to your build? If you’re working slowly over a few months, it might make sense to try to implement a new approach.

If a deadline like graduation is rapidly approaching, you might want to stick to tried and true methodologies. There’s absolutely no shame in creating Matplotlib visualizations instead of using a BI tool like Tableau. It might not be as “pretty” but if you nail the business fundamentals, your work could outshine the slickest dashboards.

To remove the guesswork from that critical decision, when to learn and when to rely on existing skills, you need a professional execution framework.

This framework is the foundation of my new ebook resource, and I'd like to offer you a free sample today. It gives you a clear, documented plan to take any project from ideation to deployment without wasting time on dead ends or pondering Catch-22 scenarios.

Thanks for ingesting,

-Zach Quinn