GlueBox Technology

Using AI to solve for User Sync

I need to build a tool to clean up some data that I am working with. I have some person/account information to sync between Web systems. My input source can be either json or csv. I have access to both formats from the upstream data provider, but I want to start with CSV files initially. The system that I am going to import the data to is Drupal 10, with Feeds and the Feeds Tamper module available to help manage the process. I can handle the Feeds import process to my Drupal system, but I need help building a cleanup tool. I need to normalize and modify the data before it's ready to import into Drupal. I would prefer to use Python, and I have access to a production-ready RHEL server that can orchestrate the "sync" process. We could start with a script to pull down the source data in json, then we process it to our needed format, and output a CSV as the first step. 

What do you think?

That sounds like a very sensible approach. Using Python to pull down your source data (whether JSON or CSV), process it, and then output a normalized CSV for Drupal’s Feeds import is both flexible and maintainable. Here are some recommendations and considerations for your project:

Modular Design:

    • Input Abstraction: Write functions or classes that can handle both JSON and CSV input. Even if you start with CSV, designing your tool to later handle JSON will make it more versatile.
    • Transformation Layer: Isolate your normalization and data-cleaning logic into a separate module. This way, if your rules change or you need to apply additional transformations (e.g., handling date formats, trimming whitespace, mapping field names), you can do so in one place.
    • Output Module: Create a function that outputs the cleaned data as CSV, ensuring it matches the structure required by Drupal’s Feeds module.

Choosing the Right Libraries:

    • Pandas: For data manipulation and normalization, Pandas is a robust choice. It provides easy-to-use functions for reading JSON or CSV, cleaning data, and exporting to CSV.
    • Standard Libraries: If your data transformations are simple, the built-in csv and json modules might be sufficient.
    • Requests: If you need to pull data from a Web service, the Requests library is great for handling HTTP requests.

Error Handling & Logging:

    • Implement robust error handling, especially when dealing with external data sources.
    • Use Python’s logging module to record the progress of the script and any issues that arise. This will help with troubleshooting when running the script on your production RHEL server.

Deployment & Scheduling:

    • Since you have access to a production-ready RHEL server, consider setting up a cron job or a systemd timer to run the sync process at regular intervals.
    • Ensure your script is idempotent or can handle incremental updates, so you don’t inadvertently process duplicate records.

Testing and Iteration:

    • Start by processing a small subset of data to validate your normalization rules.
    • Once you’re confident that the output CSV matches the requirements of the Drupal Feeds module (and the additional transformations can be handled by Feeds Tamper if needed), scale up to process the full data set.

Sample Workflow Outline in Python:

import pandas as pd
import requests
import json
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def fetch_data(url, data_format='json'):
   response = requests.get(url)
   response.raise_for_status()  # Ensure we notice bad responses
   if data_format == 'json':
       return response.json()
   else:
       # For CSV, you can directly return response.text or use StringIO
       return response.text

def process_json_data(json_data):
   # Convert JSON to a DataFrame. Adjust based on your JSON structure.
   df = pd.json_normalize(json_data)
   # Example: Normalize names, trim whitespace, fix date formats, etc.
   df['first_name'] = df['first_name'].str.strip().str.title()
   df['last_name'] = df['last_name'].str.strip().str.title()
   # Add any other normalization or mapping rules as needed
   return df

def process_csv_data(csv_text):
   from io import StringIO
   df = pd.read_csv(StringIO(csv_text))
   # Apply similar cleaning as above
   df['first_name'] = df['first_name'].str.strip().str.title()
   df['last_name'] = df['last_name'].str.strip().str.title()
   return df

def export_to_csv(df, output_path):
   df.to_csv(output_path, index=False)
   logger.info(f"Exported cleaned data to {output_path}")

def main():
   # Example URL or file path for your source data
   data_url = "https://example.com/path/to/data.json"
   try:
       # Fetch data (adjust parameters as needed)
       raw_data = fetch_data(data_url, data_format='json')
       
       # Process data based on its format
       if isinstance(raw_data, dict) or isinstance(raw_data, list):
           df = process_json_data(raw_data)
       else:
           df = process_csv_data(raw_data)
       
       # Export the cleaned data to CSV for Drupal import
       export_to_csv(df, "cleaned_data.csv")
   except Exception as e:
       logger.exception("An error occurred during data processing.")

if __name__ == "__main__":
   main()

 

Integration with Drupal:

    • Once the CSV is ready, you can use the Feeds module to import the data into Drupal.
    • If additional tweaks are needed at import time, the Feeds Tamper module can handle further modifications, such as data mapping or value transformations.

Overall, your plan to start with a JSON fetch and CSV output script is a solid first step. It will allow you to focus on the data normalization logic while keeping the system decoupled. As your needs evolve, you can expand the tool’s capabilities (like supporting direct CSV input or even RESTful updates) without overhauling your entire workflow.