Guide to data tools landscape for developers
A software engineer shares their experience transitioning into the data field and provides a high-level overview of the data tool landscape for developers. The article aims to demystify data terminology for those outside the data science discipline.
Why it matters
Helps bridge the knowledge gap between software engineering and data science, facilitating better cross-functional collaboration in tech companies.
Some time ago, I joined Deepnote as a software engineer. Deepnote makes a cloud notebook for data teams. I, however, didn't have any background in data. But I knew what a notebook was and I thought it would be interesting to work on this kind of project. I didn't think the data field was that far from software engineering, I always thought of them as adjacent fields.
Soon after joining I realized that I didn't know a thing about it! There are so many data tools besides notebooks, and I had no idea what they were used for or what the general work process in data science was. And if I don't know how various data tools are usually used, or how they interact with notebooks, I can't really suggest a good feature for a notebook or spot a problematic UI flow.
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