evidence VS kuwala

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.

---

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

--> Google Trends Data for Regions (Coming Soon)

The tool goes beyond our previously published CLI tool (https://github.com/kuwala-io/kuwala/tree/master/kuwala) by providing a hostable solution with a user-friendly interface. We have not open-sourced it yet but a demo is available here: https://geosage.kuwala.io/.

Urban planners can utilize movement data to analyze foot traffic in different city zones. Marketers can leverage demographic data to tailor campaigns more effectively. Developers can build their apps on top of it.

To round it up .... GeoSage brings...

Unified Data Management: Access data from OSM, Facebook, and soon Google, all in one place.

--> https://github.com/kuwala-io/kuwala/blob/master/kuwala/pipelines/osm-poi/README.md

Googe Popular Times: Movement data can be also found on Google. When you search a location it is often shown how frequently a place was visited on an hourly-daily basis (on an index of 0-100). With this libary you can access all the Popular Times data for location and entire cities

What are the 5 hottest dbt Repositories one should star on Github 2022?

dbt is a software framework that sits in the middle of the ELT process. It represents the transformative layer after loading data from an original source. Dbt combines SQL with software engineering principles.

Here are my top5!

- Lightdash (https://github.com/lightdash/lightdash): Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.

- ⏎ re_data (https://github.com/re-data/re-data): Re-Data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project.

- evidence (https://github.com/evidence-dev/evidence): Evidence is another tool for lightweight BI reporting. With Evidence, you can build simple reports in "medium style" using SQL queries and Markdown.

- Kuwala (https://github.com/kuwala-io/kuwala): With Kuwala, a BI analyst can intuitively build advanced data workflows using a drag-drop interface on top of the modern data stack without coding. Behind the Scenes, the dbt models are generated so that a more experienced engineer can customize the pipelines at any time.

- fal ai (https://github.com/fal-ai/fal): Fal helps to run Python scripts directly from the dbt project. For example, you can load dbt models directly into the Python context which helps to apply Data Science libraries like SKlearn and Prophet in the dbt models.

Kuwala ( https://github.com/kuwala-io/kuwala ) Kuwala is a data workspace that consolidates the Modern Data Stack and makes it usable for BI analysts and Engineers. Even though dbt is originally targeted at BI Analysts, dbt is mainly used by Engineers. This shifts a large amount of pipeline engineering effort to the IT department. With Kuwala, a BI analyst can intuitively build advanced data workflows using a drag-drop interface on top of the modern data stack without coding. Consequently, the BI Analyst can work more iteratively and maintain the complete workflow from source to metrics in a dashboard. Under the hood and Behind the Scenes, the dbt models are generated so that a more experienced engineer can customize the pipelines at any time. In addition, engineers can easily convert dbt models into reusable “drag and drop” components.

- 🧱 Kuwala: With Kuwala, a BI analyst can intuitively build advanced data workflows using a drag-drop interface on top of the modern data stack without coding. Behind the Scenes, the dbt models are generated so that a more experienced engineer can customize the pipelines at any time.

I have then built with a friend this here: https://github.com/kuwala-io/kuwala/tree/master/kuwala/pipelines/admin-boundaries . So the script is extracting the boundaries from OSM and cleans it (forms the hierachy and connect shapes). However, it did not lift up and I had not the feeling this was in the end an interesting feature for the community. It would be wonderful to hear your feedback and maybe find someone to pick it up :-)

Hi, we have a nice slack channel, here: https://kuwala-community.slack.com/ssb/redirect and my repo on Github is here available: https://github.com/kuwala-io/kuwala

**Global Admin Boundaries:** A huge problem that often people feel when working with location data is aggregating the data into different geo-based slices (country level, admin level, or even smaller into sub-districts). Here is a repo that cleaned the data out of Open Street Map for geo boundaries worldwide from very broad to a very small granularity --> https://github.com/kuwala-io/kuwala/blob/master/kuwala/pipelines/admin-boundaries/README.md

If it is about location data you should know OpenStreetMap. It's the biggest Database with meta info on location. It's not perfect but big companies like Mapbox, Apple, and Microsoft rely on it. Since the API is kind of messy, you can load with this repository whole cities information smoothly into a PostGres --> https://github.com/kuwala-io/kuwala/blob/master/kuwala/pipelines/osm-poi/README.md