Apache Superset v1.3 is out! 🚀

- Official @TheASF source release: downloads.apache.org/superset/1.3.0/
- @github https://github.com/apache/superset/tree/1.3.0
- @pypi https://pypi.org/project/apache-superset/

Deploy Apache Superset with @MySQL Database Service on OCI – https://blogs.oracle.com/mysql/deploy-apache-superset-with-mysql-database-service-on-oci #MySQL #MDS #OCI

#ApacheSuperset now supports real-time indexing database RocksetCloud 👏! Check out this new post by @SriniKadamati learn more ➡️ hubs.li/H0W-1ld0

#Superset #OpenSource #DataVisualization #BusinessIntelligence #moderndatastack #DataEngineering #dashboards #charts

Join us tomorrow for our next Apache Superset Meetup! We will focus on what's new in Superset 1.3 and what's coming down the pipe.

https://www.meetup.com/Global-Apache-Superset-Community-Meetup/events/280917393/

If you can't make it, join the Meetup group and we will send out the recording!

More Apache Project anniversaries this month: BookKeeper and Drill (7 yrs); Brooklyn, Groovy, Kylin, and REEF (6 yrs); Geode (5 yrs); Guacamole and Impala (4 yrs); Griffin (3 yrs); Petri (2 yrs); Superset and TVM (1 yr) --many happy returns #OpenSource

https://projects.apache.org/committees.html?date

Apache Superset v1.4 is out! 🚀

- Official @TheASF source release: downloads.apache.org/superset/1.4.0/
- @github https://github.com/apache/superset/tree/1.4.0
- @pypi https://pypi.org/project/apache-superset/
- changelog https://github.com/apache/superset/blob/1.4.0/CHANGELOG.md

Customized version of Apache Superset with ‘Export to .XLSX’ support was updated.

It works with versions 1.1.0, 1.3.2, and 1.4.0 now.

https://github.com/data-diving/superset-xlsx

Apache Superset 1.4.1

The official source release:
https://www.apache.org/dist/superset/1.4.1
The Pypi package:
https://pypi.org/project/apache-superset/

Superset 2.0 is a huge release and big step forward for the open source project in terms of user experience and stability. Superset 2.0:
• eliminates lots of tech debt by deprecating lots of old feature flags and code paths
• unifies the user experience in many areas of the product
• adds new features like the horizontal bar chart and revamped viz picker in Explore
• continues to improve the database experience for popular databases like Databricks, Trino, Pinot, etc.
• and much more
Join us for this virtual Superset 2.0 Meetup, hosted by the Preset.io team!

https://www.meetup.com/global-apache-superset-community-meetup/events/287351416

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

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/

Apache Superset 3.0.1 just realeased on PyPi🚀

https://pypi.org/project/apache-superset/3.0.1/

Apache Superset 3.0.2 just realeased on PyPi🚀

https://pypi.org/project/apache-superset/3.0.2/

Apache Superset 4.0 released on PyPi🚀

https://pypi.org/project/apache-superset/4.0.0/

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

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 metadv package