Apache Superset 3.0.0 is out🚀
Release notes https://preset.io/blog/superset-3-0-release-notes/
Detailed change log https://raw.githubusercontent.com/apache/superset/3.0/CHANGELOG.md
GitHub https://github.com/apache/superset/tree/3.0.0
PyPi https://pypi.org/project/apache-superset/3.0.0/
Change log https://raw.githubusercontent.com/apache/superset/3.0.0/CHANGELOG.md
Release notes https://preset.io/blog/superset-3-0-release-notes/
Detailed change log https://raw.githubusercontent.com/apache/superset/3.0/CHANGELOG.md
GitHub https://github.com/apache/superset/tree/3.0.0
PyPi https://pypi.org/project/apache-superset/3.0.0/
Change log https://raw.githubusercontent.com/apache/superset/3.0.0/CHANGELOG.md
preset.io
Superset 3.0 Release Notes
Apache Superset 3.0 release notes: major new features, improvements, and highlights from this landmark release.
Superset 2.1.1 LTS
While that new (3.0.0) major release has been tested fairly extensively, 2.1.1 is officially our “stable” release and will continue to receive patches alongside 3.0. Superset 2.X will continue to receive such patches at least until the release of Superset 3.1.0 or 4.0.0 in the future.
https://preset.io/blog/superset-2-1-1-a-more-secure-and-stable-patch-release/
While that new (3.0.0) major release has been tested fairly extensively, 2.1.1 is officially our “stable” release and will continue to receive patches alongside 3.0. Superset 2.X will continue to receive such patches at least until the release of Superset 3.1.0 or 4.0.0 in the future.
https://preset.io/blog/superset-2-1-1-a-more-secure-and-stable-patch-release/
Apache Superset 6.0.0 is a modern, enterprise-ready business intelligence web application
The official source release: https://downloads.apache.org/superset/6.0.0
The PyPI package: https://pypi.org/project/apache_superset/6.0.0
The CHANGELOG for the release: https://github.com/apache/superset/blob/6.0.0/CHANGELOG/6.0.0.md
The instructions for updating to the release: https://github.com/apache/superset/blob/6.0.0/UPDATING.md
The official source release: https://downloads.apache.org/superset/6.0.0
The PyPI package: https://pypi.org/project/apache_superset/6.0.0
The CHANGELOG for the release: https://github.com/apache/superset/blob/6.0.0/CHANGELOG/6.0.0.md
The instructions for updating to the release: https://github.com/apache/superset/blob/6.0.0/UPDATING.md
metadv — Python package and YML file specification:
• processes data entities, relations and attributes
• provides base validator class that can be inherited to add additional validations
• generates dbt models for Data Vault 2.0 (hub, links, sat and ma_sat) in popular macros formats (Datavault-UK/automate_dv and ScalefreeCOM/datavault4dbt (user can choose and even add own templates)
• can be run in CLI as well as imported into your Python code
dbt-ui — web application for collaborative work on dbt-core projects (on the screenshot):
• frontend and backend
• base git support
• model editor with SQL+Jinja syntax highlight
• table view for seed/CSV files
• rendered model code view
• DB preview for models
• model level lineage
• compilation/run and test of single models as well as the whole project
• and last but not least, visual drag&drop modeling of entities, relations & attributes with following Data Vault 2.0 models generation right in the web browser using
• processes data entities, relations and attributes
• provides base validator class that can be inherited to add additional validations
• generates dbt models for Data Vault 2.0 (hub, links, sat and ma_sat) in popular macros formats (Datavault-UK/automate_dv and ScalefreeCOM/datavault4dbt (user can choose and even add own templates)
• can be run in CLI as well as imported into your Python code
dbt-ui — web application for collaborative work on dbt-core projects (on the screenshot):
• frontend and backend
• base git support
• model editor with SQL+Jinja syntax highlight
• table view for seed/CSV files
• rendered model code view
• DB preview for models
• model level lineage
• compilation/run and test of single models as well as the whole project
• and last but not least, visual drag&drop modeling of entities, relations & attributes with following Data Vault 2.0 models generation right in the web browser using
metadv package