Intuit Build Challenge

Welcome to the Technical Assessment Repository! This repository contains implementations for various engineering coding challenges. The goal is to showcase clear, well-structured, and efficient solutions tailored to specific requirements.

Overview of Sub-Projects

This repository comprises four distinct sub-projects, each designed to test a specific area of software engineering:

1. Library Book Checkout System
   • Description: Robust object-oriented design and deterministic state management for tracking library assets and enforcing patron borrowing rules.
   • Key Features: Decoupled business logic (date calculations, fine enforcement), modern exception handling, and isolated data models.
2. Order Processing and Invoice Summaries
   • Description: Pipelined data processing to parse, validate, and aggregate textual data into properly formatted invoice reports.
   • Key Features: Efficient file stream extraction, error logging for malformed rows, and mathematical reduction for dynamic discounts and totals.
3. Producer-Consumer System
   • Description: Thread synchronization and safe concurrent data transfer between threads.
   • Key Features: Shared queue with strict capacity limits, thread orchestration using modern locking and synchronization techniques (locks, bounded queues).
4. Sales Analytics Application
   • Description: Functional programming principles and stream processing to analyze large CSV datasets.
   • Key Features: Efficient filtering, grouping, and aggregation across multiple dimensions using filter(), map(), and reduce().

Coding Language Used

• Python (Version 3.9+)

Best Practices Followed

The implementation follows modern engineering principles with an emphasis on production-readiness:

• Clean Architecture & Modularity: Complex tasks are decomposed into manageable, single-responsibility classes.
• Defensive Error Handling: Explicit handling of edge cases (e.g., malformed data, missing files, concurrency interruptions) via custom exceptions.
• Robust Data Models: Use of standard dataclasses for immutable pattern matching and strict typing.
• Concurrency Management: Proper thread synchronization avoiding active waiting and race conditions.
• Functional Programming: Efficient data streaming operations for readability and performance.
• Virtual Environment & Dependencies: Isolation using venv and package management tools (pyproject.toml) for reproducibility.
• Comprehensive Testing: Isolated pytest suites bridging edge cases, utilizing ephemeral directories for test data.
• Telemetry & Logging: Safe capture of broken states and traces into local output/ directories.
• Environment Configuration: Secure decoupling of sensitive information using .env files.

Project Navigation

For detailed setup instructions, execution commands, and testing information for each task, please visit the README.md files in their respective directories:

• ➡ Library Book Checkout System Setup & Execution
• ➡ Order Processing Setup & Execution
• ➡ Producer-Consumer System Setup & Execution
• ➡ Sales Analytics App Setup & Execution