dataset_storage_mode.py

import swifter
import json
def get_modes(dataset, workspace):
    '''
    Author: Sandeep Pawar | fabric.guru

    Returns storage mode of a dataset as identified from TMSL
    Fabric uses XMLA client so this will work only for the semantic models in Premium capacity
    Swifter is used to process a long list faster using .apply method

    Always verify the results. 

    '''
    try:
        tmsl = json.loads(fabric.get_tmsl(dataset=dataset, workspace=workspace))
        if 'model' in tmsl and 'tables' in tmsl['model']:
            modes = set(p['mode'] for table in tmsl['model']['tables'] for p in table['partitions'] if 'mode' in p)
            return modes
        else:
            ## if the model doesn't have a partition, 
            ## it's likely a default semantic model
            return "{default}" 
    except Exception as e:
        # datamart dataset doesn't support XMLA, handling that as an exception
        error_message = str(e)
        try:
            error = json.loads(error_message)
            if 'DatamartInvalidData' in str(error):
                return "Datamart Semantic Model"
        except json.JSONDecodeError:
            pass  # 
        return None



datasets['Mode/Type'] = datasets.swifter.apply(lambda row: get_modes(row['Dataset ID'], row['workspace']), axis=1)
#If you don't want to install/use swifter, use datasets.apply(...) instead
datasets