171–180 of 201 posts
Bummer that it can't pull data from JSON APIs, which Redash can do.
https://preset.io/blog/accessing-apis-with-superset/
"Shillelagh (ʃɪˈleɪlɪ) is a Python library and CLI that allows you to query many resources (APIs, files, in memory objects) using SQL. It's both user and developer friendly, making it trivial to access resources and easy to add support for new ones"
Had a very good experience with Superset. Superset allowed us to replace Tableau and not looking back Took me a while figure out how to embed it into my app using Superset Embedded SDK. Superset Embedded SDK - "Embedded SDK allows you to embed dashboards from Superset into your own app, using your app's authentication. Embedding is done by inserting an iframe, containing a Superset page, into the host application." h…
All in all, not very innovative, but highly needed open source version of a traditional BI tool. Definitely something to follow and to use in temporary, not too demanding use cases. And hopefully a future replacement of Tableau or Power BI.
Had a very good experience with Superset. Superset allowed us to replace Tableau and not looking back Took me a while figure out how to embed it into my app using Superset Embedded SDK. Superset Embedded SDK - "Embedded SDK allows you to embed dashboards from Superset into your own app, using your app's authentication. Embedding is done by inserting an iframe, containing a Superset page, into the host application." h…
We use ECharts in our open source BI tool (Evidence) and it's a great library. Has helped us build a declarative syntax for viz which can be version controlled ( https://evidence.dev ) Previous HN discussion: https://news.ycombinator.com/item?id=35645464 (97 comments)
Earlier quoted context omitted.
We use ECharts in our open source BI tool (Evidence) and it's a great library. Has helped us build a declarative syntax for viz which can be version controlled ( https://evidence.dev ) Previous HN discussion: https://news.ycombinator.com/item?id=35645464 (97 comments)
Evidence looks cool, and I evaluated sometime back. The docs says the pages are all pre-rendered for all possible combinations. Is that the case still? If so, if I have a date filter, is it going to pre-render all possible dates?
Here's an example project with some filter components and custom styling: https://ecommerce.evidence.app/
This is still a static app - the data warehouse was only hit during the app's build process
Earlier quoted context omitted.
No built-in thick semantic layer, compared to Looker. I wrote about Superset's semantic layer here: https://preset.io/blog/understanding-superset-semantic-layer... One popular option is to use dbt or Cube for the semantic layer and pair with Superset: https://preset.io/blog/announcing-presets-ui-integration-wit... and https://preset.io/blog/open-source-looker-cube-superset/
The lack of a semantic layer and join limitations are what made me pass on superset, but that was a couple years ago so maybe those features have been added. I built my own semantic layer instead. I use this in production in my company but obviously use at your own risk as it's a one-man show. https://github.com/totalhack/zillion
There is this:
> A final SQL query against the combined data from the DataSource Layer
> The Combined Layer is just another SQL database (in-memory SQLite by default) that is used to tie the datasource data together and apply a few additional features such as rollups, row filters, row limits, sorting, pivots, and technical computations.
But it leaves me with questions - how/when does this get populated? What other options are there besides in-memory SQLite? (I presume that's just a convenience for development and would use something else in production?)
Or is it just what Superset calls a 'metastore' i.e. data about the data, and the queries are run against the data source layer?
Has anyone tried both this and Metabase? I've used Metabase in a few projects and I find it very nice. This seems more powerful, perhaps? Is it worth it for BI on small datasets?
I recently ran a little shootout between Superset, Metabase, and Lightdash — all open source with hosted options. All have nontrivial weaknesses but I ended up picking Lightdash. Superset is the best of them at data visualization but I honestly found it almost useless for self-serve BI by business users if you have existing star schema. 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.
anyone knows how does it compare to Looker?