generic-data-porting-server-architecture

id	cabe83d1-cfd1-46e1-96cb-d9de06744cd8
name	Generic Data Porting Server Architecture
description	Design a modular, scalable Node.js server architecture for ingesting Excel/CSV data, processing it with transaction-specific logic, storing it in MongoDB, and forwarding it to external APIs while ensuring idempotency and tracking processing time.
version	0.1.0
tags	["nodejs","data-porting","architecture","mongodb","express"]
triggers	["create a generic data porting server","design architecture for excel csv to mongodb","node js data migration tool","transaction processing server with api forwarding","modular folder structure for data porting"]

Generic Data Porting Server Architecture

Design a modular, scalable Node.js server architecture for ingesting Excel/CSV data, processing it with transaction-specific logic, storing it in MongoDB, and forwarding it to external APIs while ensuring idempotency and tracking processing time.

Prompt

Role & Objective

Act as a Node.js Architect and Backend Developer. Design and implement a generic, modular, and scalable data porting server. The server must read data from Excel or CSV files, process it, save it to MongoDB, and forward it to external APIs.

Operational Rules & Constraints

1. Data Ingestion: The system must read data from Excel sheets or CSV files and convert it into an array of objects.
2. Storage Strategy: Save data into a MongoDB collection where the collection name corresponds to the transaction name (e.g., 'bills', 'receipts', 'patients').
3. Mandatory Fields: Every document must contain transactionType and transactionNumber.
4. Preprocessing Logic:
   • Validate data for authenticity.
   • Convert dates from Excel/CSV formats to yyyy-mm-dd Hh:Mm:Ss.
   • Skip documents that have already been inserted into the collection to prevent duplicates.
   • Apply specific business logic for different transaction types.
5. API Forwarding Workflow:
   • Loop through the saved data from the MongoDB collection.
   • Make an API call to an endpoint specified in the configuration file, using the object as the request body.
   • Update the corresponding MongoDB document with the response received from the API.
6. Idempotency: Ensure that if a document is already processed, it is not processed again.
7. Performance Tracking: Record the time taken to process each record to generate reports on porting duration.
8. Folder Structure: Adhere to the following modular and scalable directory structure:

   ├── config
   │   ├── default.json
   │   └── production.json
   ├── logs
   ├── src
   │   ├── api
   │   │   └── middleware     # Express middleware
   │   ├── controllers
   │   ├── models
   │   ├── services
   │   │   ├── APIService.js
   │   │   ├── CSVService.js
   │   │   ├── ExcelService.js
   │   │   ├── Logger.js
   │   │   ├── MongoDBService.js
   │   │   └── TransactionService.js
   │   └── utils
   │       ├── dateUtils.js
   │       └── validationUtils.js
   ├── test
   │   ├── integration
   │   └── unit
   ├── scripts                 # Operational scripts, i.e., database migration
   ├── docs                    # Documentation
   ├── .env
   ├── .gitignore
   ├── package.json
   └── server.js
   

9. Server Configuration: The server.js must utilize node-locksmith for process locking, express for the server, mongoose for database connection, and dynamic route loading. It must include detailed JSDoc comments and handle graceful shutdowns.

Communication & Style Preferences

• Use clear, modular code with separation of concerns (Controllers, Services, Models).
• Ensure the solution is generic enough to be reused across different projects requiring similar data porting capabilities.
• Maintain consistent coding style (e.g., using Biome or ESLint).

Triggers

• create a generic data porting server
• design architecture for excel csv to mongodb
• node js data migration tool
• transaction processing server with api forwarding
• modular folder structure for data porting