metriql VS rmarkdown

Some of the companies that operate in space are Cube Dev; Transform(currently acquired by dbt); metriql. See more companies at https://www.moderndatastack.xyz/companies/metrics-store.

Presto is pretty successful but its focus is to be distributed query engine, not a proxy layer for the existing query engines. We use Trino ( formerly Presto) as our query layer and do something similar to Hydra at Metriql [1] with a fairly different use-case. Data people provide a semantic layer with the mecrics and expose them to 18+ downstream tools.

[1]: https://metriql.com

This is why metrics store tooling have started appearing recently (e.g. TransformData, SuperGrain, Metriql, dbt Metrics) - to solve the problem of this table / metric disorganization across an org's data landscape.

We're using Superset to enable our analysts to explore our clients' SEM/SEO/analytics data. It also posts alerts to Slack when, say, the daily session count of a website isn't what was expected given the historical data.

Yeah, it's a little rough to get going, but once it is, we've found it to be a really powerful (and actively developed!) BI tool. It's even better with dbt + MetriQL [0], which can automatically sync Superset's dataset metadata directly with properties you set up in dbt.

Adding custom visualizations is much harder than it should be, but they're very much aware of that, and working to address it. Their Slack community is super-helpful, too.

[0]: https://metriql.com

As a current Looker power-user, this looks really solid.

One thing I’m not sure about though: can you use the metrics outside of the native tool, and if so how?

That is, I see Looker as a BI tool, not a metrics layer, since you mainly use the metrics you define inside Looker, not in other tools. On the other hand, something like MetriQL[0] is a pure metrics layer that can supposedly be used anywhere.

Is this both? If so, some better documentation around how to use the metrics layer would be helpful (or maybe I just didn’t look in the right place).

[0] https://metriql.com/

3. Define metrics in one place on top of your data models and expose the metrics to all the data tools. (This layer is new, and we're tapping it at https://metriql.com)

We use BSL license and metriql is free with a single database target. If you want to connect multiple dbt projects in a single deployment, you need to go through the sales cycle.

We work with ETL vendors that use metriql to make revenue with our BI tool integrations so we picked BSL license to be able to structure our business model in a way that you should be required to pay only if you're reselling metriql to your customers.

You can find the license here: https://github.com/metriql/metriql

I'm surprised to see no one has pointed out [RMarkdown + RStudio](https://rmarkdown.rstudio.com) as one way to immediately interface with Pandoc.

I used to write papers and slides in LaTeX (using vim, because who needs render previews), then eventually switched to Pandoc (also vim). I eventually discovered RMarkdown+RStudio. I was looking for a nice way to format a simple table and discovered that rmarkdown had nice extensions of basic markdown (this was many years ago so maybe that is incorporated into vanilla markdown/pandoc).

The RMarkdown page claims:

> R Markdown supports dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.

...which I think is largely due to using pandoc as the core generator.

RStudio shows you the pandoc command it runs to generate your document, which I've used to figure out the pandoc command I want to run when I've switched to using pandoc directly.

This is a bit of a "lazy" way to interact with pandoc. Maybe the "laziest" aspect: when I get a new computer, I can install the entire stack by installing Rstudio, then opening a new rmarkdown document. Rstudio asks whether I'd like to install all the necessary libraries -- click "yes" and that's it. Maybe that sounds silly but it used to be a lot of work to manage your LaTeX install. These days I greatly favor things that save me time, which seems to get more precious every year.

Then, I worked on a Shiny project where I had to learn R Markdown. I was very excited about it because being paid to learn a new technology is something I have always preferred. I also worked with Highcharts graphs, which I didn’t do for years. It was also the first time I was being paid to design something. I didn’t enjoy that part as much as development, but I cannot say it was a bother either.

files/figure-latex/unnamed-chunk-2-1.pdf) Try to find the following text in midterm-question.Rmd: . See https://github.com/rstudio/rmarkdown/issues/385 for more info.

We used R Markdown for cleaning and analysis, creating updateable web pages we could share with everyone involved. Similarweb’s categories were useful, but too niche for us. So we spent a lot of time recategorizing and redefining the groupings. We used the token count for each website — how many words or phrases — to measure it’s importance in the overall training data.

In [R Markdown](https://rmarkdown.rstudio.com/) or [Quarto](https://quarto.org/), I can include inline code in a math equation, e.g.,

you probably want an rmd file with html output

GitHub issue code folding

R Markdown / Quarto https://quarto.org/ https://rmarkdown.rstudio.com/ ; can dynamically generate a document and compile it to HTML, PDF, others

Hi! At this moment I'm not opening the source code, but I can explain you the tech used. This site is based on another site I created before named https://r-charts.com/ and it was created with blogdown (HUGO + R Markdown). Hence, each tutorials is an R markdown file. For PYTHON CHARTS, in order to run Python within an R markdown file I had to use an R package named reticulate. In addition, the template depends on shuffle.js for filtering and fuse.js for searching