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I was only introduced to using Python inside Jupyter Notebooks in the past few months. But it has worked well. Somewhere out there are demo pages and videos. Short and sweet - I go to "Data" and on the left side, there are three dots. Click there and you will find Jupyter Notebooks. Within your Python code you can edit, check responses, debug portions at a time, save and schedule like you do Magic ETLs. It didn't take me long after watching some video I found through Google, to muster up the courage and create a "sample" workbook. I created a Python page and hit the run icon and it did what I told it. Then I got a little more advanced.

My first attempt at your question would be something like:

pip install pandas domo-py glob2

This would install the pandas, domo, and glob libraries. Then something like:

import os

import glob

import pandas as pd

from domo import Domo



# Configuration for Domo API

DOMO_CLIENT_ID = 'your_client_id'

DOMO_CLIENT_SECRET = 'your_client_secret'

DOMO_API_HOST = 'api.domo.com'  # or use 'api.domo.com' for production



# Folder where CSV files are located

CSV_FOLDER_PATH = '/path/to/your/csv_folder'



# Domo dataset details

DATASET_ID = 'your_dataset_id'  # Replace with your actual Dataset ID


def read_csv_files(folder_path):



    # List all CSV files in the specified folder

    csv_files = glob.glob(os.path.join(folder_path, '*.csv'))


    # Initialize an empty list to hold DataFrames

    dataframes = []



    # Loop through the list of CSV files

    for file in csv_files:



        # Read each CSV file into a DataFrame

        df = pd.read_csv(file)



        # Append the DataFrame to the list

        dataframes.append(df)



    # Concatenate all DataFrames into a single DataFrame

    combined_df = pd.concat(dataframes, ignore_index=True)

    return combined_df



def upload_to_domo(df, dataset_id):



    # Initialize Domo client

    domo = Domo(DOMO_CLIENT_ID, DOMO_CLIENT_SECRET, host=DOMO_API_HOST)


    # Convert DataFrame to CSV for upload

    csv_data = df.to_csv(index=False)


    # Upload the CSV data to Domo

    domo.datasets.data_import(dataset_id, csv_data, content_type='text/csv')

    print(f'Data uploaded to Domo dataset {dataset_id} successfully.')



# Main execution

if __name__ == '__main__':



    # Read and combine CSV files

    combined_df = read_csv_files(CSV_FOLDER_PATH)


    # Upload combined DataFrame to Domo

    upload_to_domo(combined_df, DATASET_ID)

I like @MarkSnodgrass suggestion. You could also use Python scripts using the imaplib, email, and os libraries to automate the download of attachments from emails. Alternatively, third-party tools could possibly automate the download process.

Domo Workbench could probably do it, but you would need to handle the specifics for files, subfolders, and transformations.

Even though your e-mail has multiple files, you can set up multiple forwarding rules and use the dataset via e-mail connector to handle each file. You can use the attachment name expression field to differentiate which file you want to connect to. Check out the documentation in this KB article.

https://domo-support.domo.com/s/article/360042931954?language=en_US

@vaco take a look at the Amazon S3 Advanced connector. I think this will pickup multiple files.

https://domo-support.domo.com/s/article/360043436353?language=en_US