Skip to content

Apache Superset (incubating) is a modern, enterprise-ready business intelligence web application

License

Notifications You must be signed in to change notification settings

fusheng9399/incubator-superset

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Superset

Build Status PyPI version Coverage Status PyPI Join the chat at https://gitter.im/airbnb/superset Documentation dependencies Status

Superset

Apache Superset (incubating) is a modern, enterprise-ready business intelligence web application

[this project used to be named Caravel, and Panoramix in the past]

Screenshots & Gifs

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


Apache Superset

Apache Superset is a data exploration and visualization web application.

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 acts 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

Database Support

Superset speaks many SQL dialects through SQLAlchemy, a Python ORM that is compatible with most common databases.

Superset can be used to visualize data out of most databases:

  • MySQL
  • Postgres
  • Vertica
  • Oracle
  • Microsoft SQL Server
  • SQLite
  • Greenplum
  • Firebird
  • MariaDB
  • Sybase
  • IBM DB2
  • Exasol
  • MonetDB
  • Snowflake
  • Redshift
  • Clickhouse
  • Apache Kylin
  • more! look for the availability of a SQLAlchemy dialect for your database to find out whether it will work with Superset

Druid!

On top of having the ability to query your relational databases, Superset ships with deep integration with Druid (a real time distributed column-store). When querying Druid, Superset can query humongous amounts of data on top of real time dataset. Note that Superset does not require Druid in any way to function, it's simply another database backend that it can query.

Here's a description of Druid from the http://druid.io website:

Druid is an open-source analytics data store designed for business intelligence (OLAP) queries on event data. Druid provides low latency (real-time) data ingestion, flexible data exploration, and fast data aggregation. Existing Druid deployments have scaled to trillions of events and petabytes of data. Druid is best used to power analytic dashboards and applications.

Installation & Configuration

See in the documentation

Resources

Contributing

Interested in contributing? Casual hacking? Check out Contributing.MD

Who uses Apache Superset (incubating)?

Here's a list of organizations who have taken the time to send a PR to let the world know they are using Superset. Join our growing community!

About

Apache Superset (incubating) is a modern, enterprise-ready business intelligence web application

Resources

License

Code of conduct

Security policy

Stars

Watchers

Forks

Packages

No packages published

Languages

  • JavaScript 54.8%
  • Python 38.5%
  • CSS 3.3%
  • HTML 3.2%
  • Shell 0.1%
  • Dockerfile 0.1%