Major features and improvements
- Added the following new datasets:
|Works with plotly graph object Figures (saves as json file)|
Bug fixes and other changes
- Defined our set of Kedro Principles! Have a read through our docs.
ConfigLoader.get()now raises a
BadConfigException, with a more helpful error message, if a configuration file cannot be loaded (for instance due to wrong syntax or poor formatting).
run_idnow defaults to
after_catalog_createdis called, similarly to what happens during a
- Fixed a bug where
kedro jupyter notebookdidn't work if the
PYTHONPATHwas already set.
- Update the IPython extension to allow passing
reload_kedrosimilar to how the IPython script works.
kedro infonow outputs if a plugin has any
PartitionedDataSetnow supports lazily materializing data on save.
kedro pipeline describenow defaults to the
__default__pipeline when no pipeline name is provided and also shows the namespace the nodes belong to.
- Fixed an issue where spark.SparkDataSet with enabled versioning would throw a VersionNotFoundError when using databricks-connect from a remote machine and saving to dbfs filesystem.
EmailMessageDataSetadded to doctree.
- When node inputs do not pass validation, the error message is now shown as the most recent exception in the traceback (Issue #761).
kedro pipeline packagenow only packages the parameter file that exactly matches the pipeline name specified and the parameter files in a directory with the pipeline name.
- Extended support to newer versions of third-party dependencies (Issue #735).
- Ensured consistent references to
model inputtables in accordance with our Data Engineering convention.
- Changed behaviour where
kedro pipeline packagetakes the pipeline package version, rather than the kedro package version. If the pipeline package version is not present, then the package version is used.
- Launched GitHub Discussions and Kedro Discord Server
- Improved error message when versioning is enabled for a dataset previously saved as non-versioned (Issue #625).