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Ask HN: Practical guides of different industries for modelling data

2 点作者 hagg3n超过 4 年前
This has always been a pet peeve of mine. Every time I had to model the database for a business I&#x27;m not familiar with I ended up wasting dozens of hours reading wikipedia and scattered blogs to get to know it, only to just scratch the surface and having to guess most of it. Sometimes I find an open source project related to the industry and that gets me going, but it&#x27;s often particular to some country and lacks generality.<p>&quot;But isn&#x27;t it the way it&#x27;s supposed to be?&quot; you ask me. Well, kinda of. I do think to model the data for any given business you should study it a bit first. But the way I&#x27;m doing it isn&#x27;t optimal at all.<p>If you&#x27;re wondering what I mean by that I have a simple, often overlooked example: geographical addresses. If you&#x27;re just addressing your own country, it&#x27;s usually straightforward enough. But if you&#x27;re addressing several countries oh oh. My go to reference is the Google Places API and their address components[1].<p><i></i>So my question is<i></i>: is there a place where we gather data modelling guides for different industries? Like introduction to their workflow, so you understand how entities relate to one another and how to best split the data, and a glossary to name the fields appropriately? I know businesses work differently around the world but I also often find that the core idea and consequently its data stays largely the same.<p>Other industries I had to work with in the past are:<p>- Postal and courier services - Multi-level marketing model - Tourism - HR, recruiting and selection<p>For all of these I had to scramble with hundreds of Google searches to understand just enough so I could provide a decent data model. I just wish I had a guide for it.<p>Thanks and happy new year.<p>1. https:&#x2F;&#x2F;developers.google.com&#x2F;places&#x2F;web-service&#x2F;supported_types#table2

1 comment

xsist10超过 4 年前
<a href="https:&#x2F;&#x2F;schema.org" rel="nofollow">https:&#x2F;&#x2F;schema.org</a> is a good starting point for finding common models.<p>Also might be worth reading <a href="https:&#x2F;&#x2F;programminghistorian.org&#x2F;en&#x2F;lessons&#x2F;intro-to-linked-data" rel="nofollow">https:&#x2F;&#x2F;programminghistorian.org&#x2F;en&#x2F;lessons&#x2F;intro-to-linked-...</a>