How to automate transformation deployment using toolkit [Contributed by SLB]

This document describes how the script for the above-described use case is set up for use.

1. We mention the output path where we want to store the transformation yaml files Two paths are mentioned as we want to classify them based on target - classic-transformation for timeseries, asset hierarchy, events etc. whereas custom-sdm-transformation for all data model related transformation

2. There is also an option to exclude transformations related to a list of data models (exclude_dm_ext_ids)
3. There is also exclude_trans_ext_id, which lists transformations that don't need to be dumped/moved

4. Then there is a custom logic added to exclude transformations starting with 'core_', which was added for project specific requirements (Core data model related transformations were being maintained manually as they were written for SDM to CDM migration and were being maintained manually)

5. We use the pull command to pull transformations, which is mentioned the below screenshot

6. To parameterize the data model version in target, we replaced them with variable {{legacy-sdm-version}}, {{legacy-sdm-tol-version}} and {{legacy-sdm-net-version}}, which are parameters mentioned in the config files of toolkit to maintain latest data model versions being deployed. Below two functions are written for the same

7. Similarly, in sql queries, we replace hard coded data model version being used in cdf_data_models() inbuilt function with the parameter {{legacy-sdm-version}}. Below two functions are written for the same

8. For adding authentication part, the below code snippet is written