Skip to content

Latest commit

 

History

History
44 lines (30 loc) · 2.35 KB

service-data-normalization.md

File metadata and controls

44 lines (30 loc) · 2.35 KB
Raw

Backend Challenge - Data Normalization Service

Introduction

The "Data Normalization Service" challenge focuses on developing an API service that standardizes and cleanses data to ensure consistency and efficiency in data processing.

Objectives

  • Understand the importance of data normalization in database management.
  • Implement algorithms and techniques for data standardization.
  • Ensure data integrity and accuracy through normalization processes.

Instructions

  1. Objective: Develop a service that normalizes incoming data to a predefined structure or format.

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

  3. Implementation Details:

    • Endpoints:
      • Define an endpoint (/normalize) that accepts data via HTTP POST requests and returns the normalized data.
    • Data Normalization:
      • Implement algorithms to cleanse and standardize data (e.g., removing duplicates, converting data types, enforcing constraints).
      • Ensure that the normalized data adheres to a specified schema or format.
    • Validation:
      • Validate incoming data to ensure it meets required criteria before normalization.
      • Handle edge cases and exceptions gracefully.

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

    • Send POST requests to the /normalize endpoint with different data inputs and verify that the response contains the normalized data.

Possible Improvements

  • Integration with Databases: Extend the service to integrate with databases for storing and retrieving normalized data.
  • Performance Optimization: Optimize algorithms and data processing techniques for better performance.
  • Error Handling: Implement robust error handling for invalid data inputs or normalization failures.
  • Versioning: Consider versioning the API to manage changes in data normalization logic over time.
  • Scalability: Design the service architecture to handle large volumes of data and scale as needed.

Conclusion

By completing this challenge, you will gain practical experience in developing a data normalization service and learn essential practices for maintaining data quality and consistency in backend systems. Explore additional improvements and challenges to further enhance your skills.

Happy coding!