superset VS evidence

Superset is absolutely phenomenal. I really hope Microsoft eventually releases all of their customizations they made to it internally to the OS community someday.

https://www.youtube.com/watch?v=RY0SSvSUkMA

https://github.com/apache/superset/discussions/20094

I recently ran a little shootout between Superset, Metabase, and Lightdash. All have nontrivial weaknesses but I ended up picking Lightdash.

Superset the best of them at _data visualization_ but I honestly found it almost useless for self-serve _BI_ by business users. This issue on how to do joins in Superset (with stalebot making a mess XD) is everything difficult about Superset for BI in a nutshell. https://github.com/apache/superset/issues/8645

Metabase is pretty great and it's definitely the right choice for a startup looking to get low cost BI set up. It still has a very table centric view, but feels built for _BI_ rather than visualization alone.

Lightdash has significant warts (YAML, pivoting being done in the frontend, no symmetric aggregates) but the Looker inspiration is obvious and it makes it easy to present _groups of tables_ to business users ready to rock. I liked Looker before Google acquired it. My business users are comfortable with star and snowflake schemas (not that they know those words) and it was easy to drop Lightdash on top of our existing data warehouse.

Or like not being able to delete a user without running some SQL:

https://github.com/apache/superset/issues/13345

Almostl instantly run into this issue setting up a test instance of Superset. And the issue has been around for years.

Are you interested in trying out Superset, but you're intimidated by the local setup process? Worry not! Superset needs some initial setup to install locally, but I've got a streamlined way to get started - using the makefile! This file contains a set of scripts to simplify the setup process.

Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.

if they don't want to pay for powerbi, can try something like https://superset.apache.org/

In today's fast-paced data-driven world, organizations must analyze data in real-time to make timely and informed decisions. Real-time data analytics enables businesses to gain valuable insights, respond to real-time events, and stay ahead of the competition. Also, the analytics engine must be capable of running analytical queries and returning results in real-time. In this article, we will explore how you can build a real-time data analytics solution using the open-source tools Redpanda a distributed streaming platform, Apache Superset, a data visualization, and a business intelligence platform, combined with RisingWave a streaming database.

We use echarts at https://evidence.dev and have been quite happy with it. We do a lot of embedded analytics and it's worked well for us.

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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