Problem Solving Techniques for API Data Gathering and Credit Report Processing

 Problem Solving Techniques for API Data Gathering and Credit Report Processing

Effective problem-solving techniques are essential for tackling complex tasks in software development. In this blog post, we'll explore two real-world examples: gathering bankruptcy data from the PACER API and processing credit reports from a credit agency's API. We'll discuss the best ways to approach these systems, the ideal programming languages for each task, and emphasize the processes used to solve these problems.

1. Gathering Bankruptcy Data from the PACER API

Task: Create a script that connects to the PACER API to gather data on bankruptcies given the case numbers. Each request costs money, charged based on the number of pages of search results. The results need to be stored in a database.



Approach:

  • Research and understand the PACER API's documentation to learn about the query structure, limitations, and costs.

  • Identify the specific data required for the project to minimize the number of requests and reduce the cost.

  • Batch the case numbers to retrieve multiple records in a single request, if possible, to minimize the number of API calls.

  • Implement error handling and retries to ensure the script can handle API failures or interruptions gracefully.

Language Choice: Python

Reasoning: Python is well-suited for this task because it has a simple syntax, excellent support for working with APIs (using libraries like Requests and Beautiful Soup), and easy integration with various databases.


2. Processing Credit Reports from a Credit Agency's API

Task: Create a script to connect to a credit agency's API, send the required information, and iterate through the XML response to gather specific data (like credit score, inquiries, summary data, etc.). The results need to be stored in a MySQL database and returned as HTML and PDF versions of the formatted credit report.



Approach:

  • Research and understand the credit agency's API documentation to learn about authentication, data submission, and response structure.

  • Choose an appropriate XML parsing library to extract the required data from the API response.

  • Design a template for the HTML and PDF versions of the credit report, ensuring consistency and readability.

  • Implement a method to convert the HTML version of the credit report into a PDF.

  • Test the script with various credit report scenarios to ensure accuracy and reliability.

Language Choice: Python

Reasoning: Python is a versatile and powerful language, with libraries for working with APIs, XML parsing (like ElementTree and lxml), and generating HTML and PDF files (like Jinja2 and ReportLab). Additionally, Python has excellent support for interacting with MySQL databases.

In both of these examples, understanding the problem at hand and researching the API documentation were crucial first steps. Python was chosen as the programming language for its simplicity, versatility, and powerful libraries that can handle various aspects of the tasks. By carefully planning the approach, selecting the right tools, and testing the solutions, these problems can be effectively solved in a cost-efficient and reliable manner.

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