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Apache Superset is a Data Visualization and Data Exploration Platform
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agrawaldevesh c4fb7a0a87 Fix uniqueness constraints on tables table (#6718) 
Summary: Superset code enforces (in Table crud view pre_add) that the
table is unique within <database, schema, table_name). Indeed in commit
15b67b2c6c (in 2016), the model was
updated to reflect that. However, it was never ported over to a
migration.

I am fixing that in this diff. I am choosing to make this be a new
migration instead of fixing an existing one since I want to fix existing
installations also cleanly.

I also considered removing the uniqueness constraint, but that won't
work: First because anyway there are other places where the <database,
schema, table> uniqueness is enforced in code. But also, the .sql field
isn't a first citizen yet: The schema of the table is picked up from the
table-name and the sql part is only used when creating the explore
query. So indeed we want this uniqueness constraint. (Also it breaks the
unit tests in dict_import_export_tests.py)
[Perhaps it can be removed when we have true .sql support, but for now
the user would have to create a database view and he can use that as the
'table name'. That way he gets schema inference also]

Also added INFO logging to the alembic migration.
2019-01-28 22:49:31 -08:00
contrib/docker Add licenses to translations (#6732) 2019-01-22 08:21:13 -08:00
docs [docs] improve upgrading instructions (#6766) 2019-01-28 22:14:02 -08:00
install/helm/superset Add licenses to translations (#6732) 2019-01-22 08:21:13 -08:00
licenses Add cc-by 4.0 for geojson files (#6731) 2019-01-19 14:28:40 -08:00
scripts Add disclaimer and remove counter (#6738) 2019-01-22 14:18:16 -08:00
superset Fix uniqueness constraints on tables table (#6718) 2019-01-28 22:49:31 -08:00
tests [cosmetic] remove 'List' prefix from list headers (#6725) 2019-01-23 21:06:56 -08:00
.dockerignore Add licenses to translations (#6732) 2019-01-22 08:21:13 -08:00
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babel-node [adhoc-filters] Adding adhoc-filters to all viz types (#5206) 2018-06-18 15:43:18 -07:00
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CODE_OF_CONDUCT.md Add licenses to translations (#6732) 2019-01-22 08:21:13 -08:00
CONTRIBUTING.md fix #6760 (#6762) 2019-01-28 22:45:16 -08:00
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README.md Update User List in README.md (#6758) 2019-01-25 08:19:19 -08:00
RELEASING.md Add licenses to translations (#6732) 2019-01-22 08:21:13 -08:00
requirements-dev.txt Add licenses to translations (#6732) 2019-01-22 08:21:13 -08:00
requirements.txt [fix] pandas>=0.24.0 datetimelike API changes (#6765) 2019-01-27 17:29:56 -08:00
setup.cfg Add licenses to translations (#6732) 2019-01-22 08:21:13 -08:00
setup.py [fix] pandas>=0.24.0 datetimelike API changes (#6765) 2019-01-27 17:29:56 -08:00
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README.md

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

Apache Druid (Incubating)!

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!

  1. AiHello
  2. Airbnb
  3. Airboxlab
  4. Aktia Bank plc
  5. Amino
  6. Apollo GraphQL
  7. Ascendica Development
  8. Astronomer
  9. Brilliant.org
  10. Capital Service S.A.
  11. Clark.de
  12. CnOvit
  13. Dial Once
  14. Digit Game Studios
  15. Douban
  16. Endress+Hauser
  17. FBK - ICT center
  18. Faasos
  19. Fordeal
  20. GfK Data Lab
  21. Grassroot
  22. HuiShouBao
  23. jampp
  24. Konfío
  25. Kuaishou
  26. Lime
  27. Lyft
  28. Maieutical Labs
  29. Myra Labs
  30. Now
  31. PeopleDoc
  32. Ona
  33. Pronto Tools
  34. QPID Health
  35. Qunar
  36. ScopeAI
  37. Shopee
  38. Shopkick
  39. Steamroot
  40. Showmax
  41. Tails.com
  42. THEICONIC
  43. Tobii
  44. Tooploox
  45. TrustMedis
  46. Twitter
  47. Udemy
  48. VIPKID
  49. Windsor.ai
  50. Yahoo!
  51. Zaihang
  52. Zalando