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