Service_Updates is a Python-based application designed to manage and process service-related updates efficiently.

• Overview
• Python Scripts Overview
  • customers.py
  • data.py
  • hub.py
• Data Flow
• Features
• Contact

The Service_Updates project provides a structured approach to managing service updates, customer data, and data validation. It integrates functionalities to ensure efficient processing and reporting of service-related updates.

Purpose:
Manages customer-related operations, including creating, updating, and deleting customer records, ensuring data integrity and consistency.

Key Responsibilities:

• Customer Creation: Add new customer entries with unique identifiers.
• Customer Updates: Modify existing customer information, such as contact details.
• Customer Deletion: Safely remove customer records from the system.
• Data Validation: Ensure all customer data adheres to predefined formats and constraints.

Purpose:
Focuses on data manipulation and transformation, processing raw data to ensure it is clean, validated, and ready for use within the application.

Key Responsibilities:

• Data Parsing: Read and interpret data from various file formats, such as CSV and JSON.
• Data Cleaning: Identify and rectify inconsistencies or errors in the data.
• Data Transformation: Convert data into the required structures for further processing.
• Schema Validation: Ensure data conforms to the application's schema requirements.

Purpose:
Serves as the central orchestrator, integrating functionalities from customers.py and data.py. It manages the overall workflow and user interactions within the application.

Key Responsibilities:

• Module Integration: Coordinate operations between different modules.
• User Interaction: Handle command-line inputs or other user interfaces.
• Workflow Management: Oversee the sequence of operations, ensuring smooth data flow.
• Error Handling: Manage exceptions and provide appropriate feedback to the user.

1. Data Ingestion:

   • hub.py initiates the process by loading raw data through data.py.

2. Data Processing:

   • data.py cleanses and transforms the data, ensuring it meets the application's requirements.

3. Customer Management:

   • hub.py utilizes customers.py to perform operations like adding or updating customer records based on the processed data.

4. Output Generation:

   • Results are compiled and, if necessary, exported for reporting or further analysis.

• Modular Architecture: Each script has a distinct responsibility, promoting maintainability and scalability.
• Data Integrity Assurance: Comprehensive validation processes ensure the accuracy and consistency of data.
• User-Friendly Interaction: Designed to facilitate straightforward user interactions, enhancing usability.
• Extensibility: The system is structured to allow easy integration of additional functionalities or modules.

For inquiries or suggestions, please contact wjsacken.