#data-modeling — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #data-modeling, aggregated by home.social.
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#DHTbilisi today at our #dh #digitalhistory spring school in #tbilisi Dinara Gagarina @dinaraamirovna started with teaching about #datamodeling - the students made a data model about their group of people. Have a look at the results:
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These models are used in literally thousands of places in the project - they're truly the heart of the system. And this weekend I am attempting a major heart surgery to rewire them.
This includes standard migrations, data migrations, and lots and lots of refactoring throughout the project.
Terrifying stuff.
Wish me luck 😬
3/3
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🕐 2026-03-14 00:00 UTC
📰 プログラマの抱いている不動産データについての誤謬 (👍 93)
🇬🇧 Common misconceptions programmers have about real estate data modeling, from building addresses to property IDs—a 'falsehoods' guide
🇰🇷 부동산 데이터 모델링에 대한 프로그래머의 흔한 오해들—건물 주소부터 부동산 ID까지, '잘못된 가정' 가이드 -
So now I'm the sucker who has read almost all the specifications. (1 spec to go) Time to start all over again now that the overview is complete. Also time for my pension and let someone else read through it all over again. #xbrl #reporting #datamodeling #knowledge
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Power Bi Data Modeling : Creating a Relational Data Model : Microsoft Power BI Beginner to Advanced
Microsoft Power BI: From Beginner to Advanced : Creating a Relational Data Model #powerbi #datamodeling ... source
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Data modelling is the blueprint of databases and analytics. Key types include:
• Conceptual & Logical Models: Define concepts, entities, and relationships
• Physical Models: Show storage details (tables, indexes, data types)
• Entity-Relationship Models: Graphical representation of data flow
• Relational, Object-Oriented, Star Schema: Applied in databases & BI#DataModeling #DataEngineering #SQL #Database #BusinessIntelligence #BigData
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Future of Interlis: The #Interlis Discourse Forum currently hosts a discussion among experts reflecting on strengths and weaknesses and the future of the #datamodeling and #dataexchange #standard. Meanwhile, materials for the upcoming Interlis User Group Meeting were published today, continuing the community’s dialogue on Interlis development and practice. https://spatialists.ch/posts/2025/10/29-future-of-interlis/ #GIS #GISchat #geospatial #SwissGIS
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Mastering Data Modeling in Power BI – Full Course [ Zero To Hero ]
powerbi #datamodeling #powerbidatamodeling #modeling #powerquery #powerbitutorial Hey, Everyone Welcome back to our ... source
https://quadexcel.com/wp/mastering-data-modeling-in-power-bi-full-course-zero-to-hero/
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Data Modeling Guide for Real-Time Analytics with ClickHouse
https://www.ssp.sh/blog/practical-data-modeling-clickhouse/
#HackerNews #DataModeling #RealTimeAnalytics #ClickHouse #TechGuide #DataScience
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A survey of data modeling approaches.
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#Veranstaltungshinweis:
Wenn Sie als Wissenschaftler:in unserer Fachcommunity mit #Archivmaterial arbeiten, könnte diese Veranstaltung für Sie interessant sein:
7. Juli 2025 Gastvortrag: Constructed Pasts and Small Stories in Digital History#datamodeling #digitaledition #constructedpast #digitalhistory
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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`. -
I wonder if a publicly available datamodel for project management solutions exists
it's be so useful 😕
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