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Backend Challenge - Analytics API

Introduction

The "Analytics API" challenge focuses on building a RESTful API that collects, processes, and presents analytical data from your application.

Objectives

  • Set up a web server capable of handling API requests for analytics data.
  • Implement endpoints for collecting events, processing metrics, and retrieving analytics reports.
  • Support real-time and batch data processing for different types of analytics.
  • Understand HTTP methods (POST, GET) and status codes.

Instructions

  1. Objective: Develop an Analytics API that provides functionality for tracking and analyzing application metrics.

  2. Environment Setup: Choose your preferred programming language (e.g., JavaScript, Python, Java, Go) and set up the necessary environment.

  3. Implementation Details:

    • Define endpoints:
      • POST /events: Collect events and user actions from your application.
      • GET /metrics/{metricId}: Retrieve metrics and analytics data for a specific metric.
      • GET /reports/{reportId}: Generate and retrieve analytical reports based on predefined metrics.
    • Implement data processing pipelines for real-time and batch analytics:
      • Real-time: Process incoming events and update real-time dashboards or metrics.
      • Batch: Aggregate data periodically to generate historical reports and insights.
    • Store analytics data in a scalable and efficient database or data store (e.g., SQL, NoSQL).
    • Ensure data privacy and compliance with regulations (e.g., GDPR) for handling user data and analytics.

  4. Testing: Test your API using a HTTP client (e.g., Postman, curl).

    • Send test events and verify their processing and storage.
    • Retrieve metrics and reports to validate data accuracy and completeness.
    • Validate API responses include appropriate status codes and error handling for edge cases.

Possible Improvements

  • Visualization: Integrate with data visualization tools (e.g., Grafana, Tableau) for interactive dashboards.
  • Predictive Analytics: Implement machine learning models for predictive analytics and recommendations.
  • Integration: Integrate with third-party analytics platforms (e.g., Google Analytics) for cross-platform insights.
  • Alerting: Implement alerting mechanisms based on predefined thresholds or anomalies detected in data.
  • Data Retention Policies: Implement policies for data retention and archival based on regulatory requirements.

Conclusion

By completing this challenge, you will gain practical experience in developing an Analytics API and learn essential practices for backend development related to tracking, processing, and presenting analytical data. Explore additional improvements and challenges to further enhance your skills.

Happy coding!