CherryPy
Django
Flask
web2py
Bottle
Tornado
Pyramid
BlueBream
Looker
Tableau
Sisense
Microsoft Power BI
Domo
Qlik
Google BigQuery
Google Analytics
Looker is a business intelligence platform with an analytics-oriented application server that sits on top of relational data stores. The Looker platform includes an end-user interface for exploring data, a reusable development paradigm for creating data discovery experiences, and an extensible API set so the data can exist in other systems. Looker enables anyone to search and explore data, build dashboards and reports, and share everything easily and quickly.
CherryPy| Categories |
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|---|---|
| Website | cherrypy.org |
| Pricing URL | - |
Looker| Categories |
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|---|---|
| Website | looker.com |
| Pricing URL | Official Looker Pricing |
Based on our record, Looker should be more popular than CherryPy. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Generally, what needs to be done to create an Django/Electron app is to package (I'm using pyInstaller)the Django app into an stand-alone executable and then bundle that into an Electron app. The question is which server should be used for this case to server Django before packaging it with pyInstaller? At the moment I'm using cherryPy as a WSGI web server to serve Django. Source: about 2 years ago
I know there are plenty of questions about Flask and CherryPy and static files but I still can't seem to get this working. Source: about 2 years ago
Then in the "foldername" you can have 5 folders, each one for each of the groups. This means that when group1 enters looker.com, his default page will be the "foldername", which contains group1folder (he cannot see the rest of the folders if you have set the permissions correctly for each folder). Source: about 1 year ago
Even if you want to make Wide Tables, combining fact and dimensions is often the easiest way to create them, so why not make them available? Looker, for example, is well suited to dimensional models because it takes care of the joins that can make Kimball warehouses hard to navigate for business users. - Source: dev.to / over 1 year ago
We take daily snapshots of test results, aggregate them, and send Looker dashboards to the appropriate teams. - Source: dev.to / about 2 years ago
Dashboard: I like to use Datastudio because it's easy (just like using google sheets), but you can also try out Looker. Source: over 2 years ago
For Growth and larger, I would recommend Looker. The only reason I wouldn't recommend it for the smaller company stages is that the cost is much higher than alternatives such as Metabase. With Looker, you define your data model in LookML, which Looker then uses to provide a drag-and-drop interface for end-users that enables them to build their own visualizations without needing to write SQL. This lets your... - Source: dev.to / over 2 years ago
Django - The Web framework for perfectionists with deadlines
Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
Flask - a microframework for Python based on Werkzeug, Jinja 2 and good intentions.
Sisense - The BI & Dashboard Software to handle multiple, large data sets.
web2py - Web2py is an open source web application framework.
Microsoft Power BI - BI visualization and reporting for desktop, web or mobile
