File Naming Conventions for Backend Analytics

When naming your files, especially those related to backend data and analytics, clarity and consistency are key. Here's a breakdown of considerations for each file type and some naming recommendations:

General Principles:

Specific File Types and Naming Recommendations:

JSON (.json):

Since JSON files store data, their names should reflect the data they contain.

Examples:

        site-analytics.json
        user-activity.json
        product-views.json
        daily-metrics.json
        api-responses.json
        2024-01-01-site-analytics.json (Date Specific)
    

Python (.py):

Python files will likely contain the logic that generates or processes your analytics data.

Examples:

        analytics_processor.py
        data_aggregation.py
        report_generator.py
        api_endpoints.py
        database_interactions.py
    

JavaScript (.js):

JavaScript files might be used for client-side analytics tracking or for server-side logic (if using Node.js).

Examples:

        analytics-tracking.js (client-side)
        analytics-server.js (server-side with Node.js)
        data-visualization.js
        api-client.js
    

Database related files (.sql):

If you have sql files for database queries, those should also be named descriptively.

Examples:

        daily_active_users_query.sql
        product_view_count.sql
    

For your specific case (backend data and analytics):

Given that you're dealing with backend data and analytics, focus on descriptive names for your JSON files. For example:

        daily-analytics.json
        site-metrics-YYYY-MM-DD.json
        user-activity-log.json
        api-response-from-external-service.json
    

Key Takeaway:

Prioritize clarity and consistency. Choose names that accurately reflect the file's contents and adhere to a consistent naming convention. This will make your codebase easier to maintain and understand.