Apache Superset is a Data Visualization and Data Exploration Platform

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Yongjie Zhao 23263da9f3 feat(explore): add tooltip to timepicker label (#12401 )		2021-01-10 21:52:02 -08:00
.github	chore: rename docker image in build_docker_image.sh, docker-compose.yml and helm values.yaml (#12337 )	2021-01-10 09:29:17 -08:00
RELEASING	docs: Added release notes and associated media for 0.38 (#12022 )	2021-01-06 13:03:54 -08:00
docker	feat: Added setup for running Cypress tests in docker locally (#11207 )	2020-12-14 08:06:19 -08:00
docs	docs: remove (some) references to incubating/incubation (#12284 )	2021-01-06 13:40:40 -08:00
helm/superset	chore: rename docker image in build_docker_image.sh, docker-compose.yml and helm values.yaml (#12337 )	2021-01-10 09:29:17 -08:00
requirements	feat(explore): time picker enhancement (#11418 )	2020-12-17 18:27:21 -08:00
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superset	test: rename column names for birth_names dataset (#12082 )	2021-01-08 14:13:20 -08:00
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README.md

Superset

A modern, enterprise-ready business intelligence web application.

Screenshots & Gifs

Gallery

View Dashboards

Slice & dice your data

Query and visualize your data with SQL Lab

Visualize geospatial data with deck.gl

Choose from a wide array of visualizations

Why Superset

Superset provides:

An intuitive interface to explore and visualize datasets, and create interactive dashboards
A wide array of beautiful visualizations to showcase your data.
Easy, code-free, user flows to drill down and slice and dice the data underlying exposed dashboards. The dashboards and charts act as a starting point for deeper analysis
A state of the art SQL editor/IDE exposing a rich metadata browser, and an easy workflow to create visualizations out of any result set.
An extensible, high granularity security model allowing intricate rules on who can access which product features and datasets. Integration with major authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USER, ...)
A lightweight semantic layer, allowing to control how data sources are exposed to the user by defining dimensions and metrics
Out of the box support for most SQL-speaking databases
Deep integration with Druid allows for Superset to stay blazing fast while slicing and dicing large, realtime datasets
Fast loading dashboards with configurable caching

Supported Databases

Superset speaks many SQL dialects through SQLAlchemy - a Python SQL toolkit that is compatible with most databases. Here are some of the major database solutions that are supported:

postgresql mysql mssql-server db2 mariadb oracle firebird exasol apache-kylin

A complete list of supported databases can be found here.

Installation and Configuration

See in the documentation

Get Involved

Ask and answer questions on StackOverflow
Join our community's Slack and please read our Slack Community Guidelines
Join our dev@superset.apache.org Mailing list

Contributor Guide

Interested in contributing? Check out our CONTRIBUTING.md to find resources around contributing along with a detailed guide on how to set up a development environment.