New improvement round for the Data Warehouse Automation ecosystem
Today, a new set of changes -all part of the ecosystem for Data Warehouse Automation– have been formally published as new releases on Github.
As always a large amount of work has been done in the background, and thanks go out to various projects and teams for providing input and suggestions.
This round of improvements concerns the TEAM (management of source-target mapping metadata) application, the Virtual Data Warehouse (VDW) pattern management tool and the Data Warehouse Automation class library (DLL).
What’s new?
The Data Warehouse Automation schema definition and class library (including the command-line utility, examples and validation features) has received an interesting tweak, courtesy of Martin Guenther, to load/map (deserialise) Json files regardless of their structure.
Previously, the Json files were required to match the structure of the Data Warehouse Automation schema. With this change any Json file can be consumed and, as long as there is a matching code generation pattern, can be used for whatever purpose and as such called from the command line or applications such as Virtual Data Warehouse.
If Json conformance to the schema definition is required, the validation calls can still be applies as part of this process.
For TEAM, a large number of bugfixes and usability improvements have been added including better handling of Data Object names that include schemas and reuse schema names that are set in the connection tabs.
Also, initial Presentation Layer examples are added. These were already available for VDW, but can now be generated from TEAM also. One cool feature is to right-click on the grid for a Presentation Layer object and generate the Json from there, something that will be rolled out across all types.
If you use this version, don’t forget to refresh the repository (Deploy Metadata Repository in the Repository screen). This will only rebuild components that are used for processing and will not remove metadata that has been entered by users – the repository can be rebuilt safely.
The Virtual Data Warehouse release contains some more quality-of-life fixes such as delays on the metadata refresh (this can consume the application when large amounts of metadata are refreshed), better logging and exception handling.
More visibly, some of the examples have been updated to match the changes in the Data Warehouse Automation definition (in the Dimension pattern).
Where can I find the latest version?
These latest releases can be found on Github, also including the detailed change log:
