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#datamodeling

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@LinusWeidickFrische @MapAmore

[Fair warning: I am speaking in an confident tone below; however, my accumulated knowledge of the data modeling philosophy behind OSM has largely occurred via osmosis, as I have slowly contributed and read more and more. I have not had formal, foundational education/training on the matter, so what follows might actually be somewhat scatterbrained.]

Presumably, the `_ref` bit was intended as an indicator of a "reference" value for the aforementioned `crossing`. E.g., `crossing_ref=zebra` where `crossing_ref` is the key, and `zebra` is the value, of the tag. In today's tag schema though, such tag keys are "flat" or "flattened," when they could instead be scoped/namespaced with colons: `crossing:markings=zebra` (presumably, one could have done `crossing:ref=zebra` as well, though `ref` has less meaning than `crossing`).

However, #osm eventually coalesced around a tree structure for their tag keys, with branches indicated by the colon (`:`) character and its subsequent string, and values for the last descendant/child node encoded as the tag's value. (I mean, the tags *might* be more general? Maybe there are cyclic graphs? But so far I haven't seen them (or at least, have not recognized them as such)). In theory, the underscore character (`_`) could have served the purpose of indicating branches as well, but... the English language uses spaces; tags cannot have spaces; underscores come closest to indicating spaces; ergo, use underscores as spaces and not as an indicator for a branch.

Incidentally, this approach also permitted:

1. namespacing of tags. E.g., one could namespace/scope specific tags so as to organize data. E.g., `us:tx:tarrant` could indicate the anything following (perhaps, `:parcel` to indicate a parcel number) relates to Tarrant county, in the state of Texas, in the United States.
2. having specific "branches" contain common semantic intent regardless of where in a tag's key tree they fall, such as `:height`. I.e., `building:height` or `antenna:height` or `building:antenna:height`.

An analysis of 100 Fortune 500 job postings reveals the tools and technologies shaping the data engineering field in 2025. Top skills in demand:
⁕ Programming Languages (196) - SQL (85), Python (76), Scala (14), Java (14)
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⁕ Data Modeling and Warehousing (83) - Data Modeling (40), Data Warehousing (22), Data Architecture (21)
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...

#DataEngineering #BigData #SQL #Python #ETL #AWS #CloudComputing #Spark #DataModeling #DataWarehouse #DevOps #DataGovernance #DataVisualization #MachineLearning #API #Scala #Java #GCP #Azure #Hadoop #Git #CICD #Terraform #DataQuality #Tableau #PowerBI #Collaboration #Microservices #MLOps #TechSkills

reddit.com/r/dataengineering/c

🎶A big thanks again to the BACH and eLaute projects 💻 We had two very fruitful discussions during our online meetings over the last weeks and could discuss some common problems in editing and data modeling of our individual projects. Looking forward to future exchanges! 🤝🛠️

🖥️ BACH: saw-leipzig.de/de/projekte/for
🖥️ eLaute: e-laute.info/
#GuDiE #BACH #eLaute #Lute #DigitalHumanities #PerformanceData #DataModeling #OpenSource #DigitalEdition #Musicology #MusicHistory

Are you ready for this year’s Advent of Code? I wrote up detailed solutions to five days from 2023 — ten problems — highlighting parsing, data modeling, analysis, and optimization techniques that you can use in your own attempts. (Link includes a HALF OFF coupon for the full set visible once it’s added to your cart!)

https://voyagesincode.com/discount/genserver_advent?redirect=%2Fproducts%2Fhow-to-train-your-scrappy-programmer

voyagesincode.comHow to Train Your Scrappy Programmer

[Coming next Tuesday!]
A data fire 🔥 you’ll actually want to get close to!

- Broken data models are going unnoticed
- Blindly adopting SE best practices isn't cutting it

Join a Fireside chat with:

- @clkao of datarecce.io
- Noel Gomez of datacoves.com

When
08/27 09:00AM PDT

Register for free:
us02web.zoom.us/meeting/regist

Includes an interactive Q&A session.

See you there!

Dear swarm intelligence, I am currently struggling a little with the modeling of a person class. According to 'Falsehoods Programmers Believe About Names'[^1] one shouldn't assume (more or less) anything about naming conventions - makes sense - but on the other hand I need a somewhat defined structure to work with this data. Does anyone has some sensible best practices to model this elegantly?

[^1]: kalzumeus.com/2010/06/17/false

www.kalzumeus.comFalsehoods Programmers Believe About Names | Kalzumeus SoftwareClassic essay about how software routinely bumbles human names.

Ich suche einen Artikel oder ein Kapitel, dass die Probleme mit (digital humanities) Daten einmal benennt, also die Problematik von Atomatisierung (komplexer Kontextdaten) , Normierung (z. B. verschiedener Schreibweisen) , Auflösen von Co-Referenzen und Mehrdeutigkeiten, eindeutige Identifizierung von Entitäten, Konfrontation mit unvollständigen, unsicheren, Fehl- und Falschinformationen. Gerne auch englischsprachig #dh #DigitalHumanies #datamodeling

🧵 1/ The initiated already know what happens today, but do YOU? It's #ResearchSupportPartnershipUiO #Wednesday! I started my day w/ a visit to the archaeologists, who are housed in a different part of the campus than the historians. I guided a researcher through setting up and exploring #Gephi for #NetworkAnalysis of #AncientGreek tombs (or the stuff therein). It will be part of a newly developed seminar for archaeology students. We talked #ContextOfDiscovery #DataModeling & basics of #HNA