1,003 karma · joined January 13, 2023
interests mostly: data engineering, open source, writing, obsidian, and neovim.
personal blog: ssp.sh second brain: brain.ssp.sh de vault: vault.ssp.sh book: dedp.online newsletter: subscribe.ssp.sh
socials: bsky.app/profile/ssp.sh, twitter.com/sspaeti, linkedin.com/in/sspaeti, https://github.com/sspaeti/
[1] https://github.com/mfontanini/presenterm
[2] https://www.ssp.sh/brain/markdown-presentations-or-slides/
> The enduring power of pivot tables is their robustness, simple usage, and fast, interactive response. It's the Lingua Franca of data if you are not fluent in the language of SQL or Python. A common language everyone understands: the top management, domain experts, and developers. It's an interface to data; it's the first no-code interface. Instead of the multidimensional query language MDX or the newer DAX, people can use a simple drag-and-drop interface. It democratized data analysis.
And in the native apps, such as Terminal, Obsidian, etc., you can just use ctrl++ or ctrl+-. At least that is how i use it. Or what specific apps you need that?
But also, as I stuck with Omarchy, I wanted a more beefier machine, as I work all day on the laptop. So with the new machine, Tuxedo latest version, I don't have any fans anymore too, just because it's much faster and better thermal. I will eventually write these up in a second part of the article.
My comparison was with Windows PCs, that always were super slowish after 2-3 years. The built quality always felt cheap. The battery was done after 2 years. Maybe it was also an unfair comparison, that I bought cheaper PCs, but at work I recently had to a dev HP laptop much later, and I had a very similar experience.
So maybe the problem is more windows than the PCs, but if you have used MacBooks, then you definitely know the difference. Running a Lenovo now, I love the much other things. Let's see how long it holds. ThinkPads are defenitely in a similar categories as Macbooks, kind of unbreakable. Love them too.
> A semantic layer is an interface to data stores that is designed to be queryable in terms relevant and familiar to those with knowledge of the business domain.
Sounds good to me, but I think it's too simplified. A semantic layer, IMO, does more. See Julian Hyde's definition, which is also similar to mine, and more involved as well:
> A semantic layer, also known as a metrics layer, lies between business users and the database, and lets those users compose queries in the concepts that they understand. It also governs access to the data, manages data transformations, and can tune the database by defining materializations.
> Like many new ideas, the semantic layer is a distillation and evolution of many old ideas, such as query languages, multidimensional OLAP, and query federation.
I appreciated your feedback. Will think a little more about it.
I curate some more on here in case of interest: https://www.ssp.sh/brain/data-modeling-languages.
> There's a lot of information out there, including from myself about the history and rise [2022], comparing it to an MVC-like approach, or explaining its capabilities. That's why in this article I focus on the why and showcase how to use it in a practical example in the next chapter.
[1] https://www.ssp.sh/blog/rise-of-semantic-layer-metrics/ [2] https://cube.dev/blog/exploring-the-semantic-layer-through-t... [3] https://cube.dev/blog/universal-semantic-layer-capabilities-...
My one line definition that I use atm:
> A semantic layer acts as an intermediary, translating complex data into understandable user business concepts. It bridges the gap between raw data in databases (such as sales data with various attributes) and actionable insights (such as revenue per store or popular brands). This layer helps business users access and interpret data using familiar terms without needing deep technical knowledge. https://www.ssp.sh/brain/semantic-layer#semantic-layer-defin...
Edit: I'm the OP.
Check it out here: https://github.com/unkyulee/micro-journal/blob/main/micro-jo...