1 - From Power BI Desktop, create a role for the sales associates.
2 - From Tabular Editor, set Object Level Security to None for the Customer[Email]...
3 - From Power BI Desktop, publish the dataset to the Sales Analytics workspace.

Reference:
https://matplotlib.org/stable/gallery/lines_bars_and_markers/bar_stacked.html
https://matplotlib.org/stable/api/legend_api.html

display(df) statistic details
You can use display(df, summary = true) to check the statistics summary of a given Apache Spark DataFrame that include the column name, column type, unique values, and missing values for each column. You can also select on specific column to see its minimum value, maximum value, mean value and standard deviation.

Optimize datasets for write operations by enabling large models
When using the XMLA endpoint for dataset management with write operations, it's recommended you enable the dataset for large models. This reduces the overhead of write operations, which can make them considerably faster. For datasets over 1 GB in size (after compression), the difference can be significant.
Tabular Editor supports Azure Analysis Services and Power BI Premium Datasets through XMLA read/write.
Note: Tabular Editor - An open-source tool for creating, maintaining, and managing tabular models using an intuitive, lightweight editor. A hierarchical view shows all objects in your tabular model. Objects are organized by display folders with support for multi-select property editing and DAX syntax highlighting. XMLA read-only is required for query operations. Read-write is required for metadata operations.
Reference:
https://tabulareditor.github.io/

Development Process Requirements
Litware identifies the following development process requirements:
SQLDW and datalake1 will act as the development environment. Once feature development is complete, all entities in synapseworkspace1 will be promoted to a test workspace, and then to a production workspace.
Power BI content must be deployed to test and production by using deployment pipelines.
Create deployment rules
When working in a deployment pipeline, different stages may have different configurations. For example, each stage can have different databases or different query parameters. The development stage might query sample data from the database, while the test and production stages query the entire database.
When you deploy content between pipeline stages, configuring deployment rules enables you to allow changes to content, while keeping some settings intact. For example, if you want a dataset in a production stage to point to a production database, you can define a rule for this. The rule is defined in the production stage, under the appropriate dataset. Once the rule is defined, content deployed from test to production, will inherit the value as defined in the deployment rule, and will always apply as long as the rule is unchanged and valid.
You can configure data source rules and parameter rules.
Incorrect:
Not B: if you already have a steady production environment, you can deploy it backward (to Test or Dev, based on your need) and set up the pipeline. The feature is not limited to any sequential orders.