evidence VS metriql

It’s interesting to me how far you have pushed the SQL language in this framework, such that it truly is “SQL only”.

The challenge as I see it with enabling analysts to build websites is that you need to build abstractions to get from familiar (SQL, yaml) - the language of analytics, to new (HTML, CSS, JS) - the language of the web browser

As one of the maintainers of Evidence (https://evidence.dev), one of the things I’ve often considered is how accessible our syntax is to analysts. Our syntax combines SQL and Markdown, with MDX style components e.g.

The are inherently webdev-ey, and I do think they put off potential users.

On the flip-side, by adhering to web standards, you get extensibility out of the box, and working out what to do is just a Google search away.

Anyway, thanks for the thought provoking piece.

Dataclips was my first experiences writing SQL.

Writing code was a markedly better DX that building dashboards in Tableau, which is why I'm now working on https://evidence.dev - a SSG for creating data from SQL and markdown

Previous HN discussions:

I'm one of the founders of Evidence (https://evidence.dev) - would be great to hear about your experience. Reaching out now!

Full fledged BI tools like Superset and Metabase are amazing for their intended use cases.

But they may be an overkill if your primary use case is to infrequently build semi-interactive reports for non-technical end-users and your use cases are are mostly covered by standard graphs & tables. Esp. so if you are familiar with SQL and have access to the underlying data source. Two nifty utilities I have found to be very useful for latter kind of use cases are SQLPage and Evidence.

They make it very convenient to whip out some SQL and convert that to a neat professional looking web ui that can be forwarded to an end user. In case of Evidence it is a statically generated site, and in case of SQLPage it is a web app that connects to a live database.

SQLPage: https://sql.ophir.dev/

Evidence: https://evidence.dev

We used ECharts to build our charting library at Evidence and it’s been a great experience overall (https://evidence.dev).

We started with D3 and a few other tools, but felt that we get a lot more out of the box with ECharts, like interactivity and an events API. ECharts is also a lot more extensible than people give it credit for.

If anyone is curious, we documented the process of selecting a charting library after assessing several options: https://github.com/evidence-dev/evidence/issues/136

The new direction seems very similar to what evidence has been doing for a while

https://evidence.dev

I'm quite excited about this, and would also love to have it distributed as an NPM package.

I work on an OSS web framework for reporting/ decision support applications (https://github.com/evidence-dev/evidence), and we use WASM duckDB as our query engine. Several folks have asked for PRQL support, and this looks like it could be a pretty seamless way to add it.

> Not sure the language you choose matters as much as making the API usable by a wide audience.

Fully agree with this, and having typeset my masters thesis and later my resume using LaTeX, I think that the “authoring experience” is 100% the place to focus on improving.

If you’re interested in the “markup to document publishing” space, you might also be interested in the open-source report publishing tool I’m now working on, Evidence (https://github.com/evidence-dev/evidence)

It’s similarly based on markdown, though uses code fences to execute code, HTML style tags for charts and components, and {…} for JavaScript, i.e.

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I'm working on an OSS BI tool focused on a document form factor. Might be of interest to you. https://github.com/evidence-dev/evidence

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