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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."
https://github.com/apache/superset/tree/master/superset-embe...
Superset is based on very high quality and well maintained chart library eChart
https://echarts.apache.org/examples/en/#chart-type-linesG
Community Roadmap
https://github.com/apache/superset/projects?query=is%3Aopen
Huge respect to Preset.io and its team for contributing to the project and keep it in a great shape
https://preset.io/blog/
Superset source code is very easy to read and understand, and as a result it's possible to implement some advanced caching techniques reduce the load on charts.
No BI is perfect.
Watching Superset for years gives me confidence the project will work as supposed down the road, and eventually some of its packages can be reusable for all kind of visualizations and data hacking.
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InfluxDB
InfluxDB β Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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evidence
Business intelligence as code: build fast, interactive data visualizations in SQL and markdown
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
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We've built a Kubernetes Operator for Apache Superset at Stackable: https://github.com/stackabletech/superset-operator/
It's part of our Open Source Data Platform and it's one of the few open source BI tools out there and there are not a lot of alternatives in this space. We generally like it.
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https://github.com/blacksmithgu/obsidian-dataview
This whole ideas to have data, visualisations and knowledge base in one private offline place is very appealing
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openobserve
π 10x easier, π 140x lower storage cost, π high performance, π petabyte scale - Elasticsearch/Splunk/Datadog alternative for π (logs, metrics, traces, RUM, Error tracking, Session replay).
eCharts is awesome. We moved from plotly after using it for several months to echarts at https://github.com/openobserve/openobserve and are super happy.
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If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.
I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.
I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.
For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.
Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).
The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.
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cube.js
π Cubeβs universal semantic layer platform is the next evolution of OLAP technology for AI, BI, spreadsheets, and embedded analytics
We use https://cube.dev/ as intermediate layer between data warehouse database and Superset (and other "terminal" apps for BI like report generators). You define your schema (metrics, dimensions, joins, calculated metrics etc) in cube and then access them by any tool that can connect to SQL db
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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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
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It should be possible (have not tried myself):
https://preset.io/blog/accessing-apis-with-superset/
"Shillelagh (ΚΙͺΛleΙͺlΙͺ) 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"
https://github.com/betodealmeida/shillelagh
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> YAML, pivoting being done in the frontend, no symmetric aggregates
(one of the maintainers of Lightdash) You touched on some of our most interesting problems here! Would be especially interested to hear about what you liked / didn't like about symmetric aggregates in Looker and how you find dev with YAML. If you have an idea of how you'd like these to look in Lightdash, the team would be really open to making that a reality.
For pivoting in the backend, this is coming! Issue here: https://github.com/lightdash/lightdash/issues/2907
