Extract. Transform. Read.

A newsletter from Pipeline​

Hi past, present or future data professional!

I recently participated in a technical design meeting that was derailed by a single, fundamental question.

“Why?”

Despite the fact that I worked with the particular data source we were discussing for nearly two years, I fell into the common trap of going “on autopilot” and failing to question the initial need for the data. At this point, you would think asking “why” of years’ worth of work would be offensive.

Instead of myself or other team members getting defensive, it led to a productive conversation about not just refining our approach to ingestion, but also inspired talk of how we can manage stakeholder expectations and softly encourage them to “do more with less.”

Fortunately, you don’t need to derail a meeting to leverage what I call a productive why. Asking occasional, tactful “whys” can position you as a critical thinker and thought leader (or at least an enthusiastic thought contributor) within your org. When appropriate, consider asking…

• Why are we using x tool over y when x clearly offers a more streamlined integration with our data warehouse?
• Why are we dedicating development resources to solving this issue when there isn’t a clear business outcome?
• Why are stakeholders asking for a new data pipeline when this existing table provides nearly all of the dimensions they’re seeking?
• Why are we paying for x service when we could feasibly build our own solution?

I realize you may not be in a professional role; nonetheless, I’ve found a lot of value can result from occasionally asking “why” even when you’re simply writing code.

For instance, I was a habitual user of Pandas’ .append() method. Unfortunately, to my disappointment, Pandas 2.0 deprecated .append() in the past year. I easily could have panicked and said “Iterating and appending key values to an empty data frame is how I’ve always converted JSON to a data frame. What am I going to do?” But being forced to adapt to the change made me think about what prompted that habit initially.

To learn what that motivation was plus how a simple "why" nearly left me tongue-tied in an interview, read the latest on Pipeline.

And so you don’t have to question where those hyperlinks go, here they are as plain text.

• ​Data Engineering Requirements Gathering​
• ​6 Months Ago I Built My Own Data Infra. Why I’m Now Tearing It Down.​
• ​From 0 To Data Solution In 5 Phases To Land Contracts & Wow Bosses​
• ​Pandas’ 2.0 Release Deprecated Your Favorite Method. What Now?​
• ​Why You Should Use JSON, Not Pandas, In Your First Data Science Project​

Questions? zach@pipelinetode.com​

Thanks for ingesting,

-Zach Quinn