This repository serves as the central hub for my CS-499 Computer Science Capstone ePortfolio. It showcases enhanced versions of key projects developed throughout my degree program, demonstrating growth in three major areas of computer science:
Each enhancement reflects both my technical development and my readiness for a professional data engineering role.
Original Artifact (CS-340 – Pre-Enhancement):
https://github.com/MarioFred-snhu/CS-340-Final-Project
This repository contains the original implementation of the Grazioso Salvare Animal Data Dashboard prior to all CS-499 enhancements.
Enhanced Artifact (CS-499 – Capstone):
https://github.com/MarioFred-snhu/data-engineering-eportfolio-artifact
This repository contains the enhanced version developed for the CS-499 Capstone, incorporating improvements in software design, algorithm efficiency, and database analytics.
| Category | Focus | Enhancement Summary |
|---|---|---|
| Software Design & Engineering | Object-Oriented Refactoring | Refactored CRUD and ETL logic into modular classes (ETLManager, DataLoader, DashboardController), improving scalability, maintainability, and security through environment variables and structured logging. |
| Algorithms & Data Structures | ETL & Data Processing Efficiency | Implemented optimized merge and transformation logic using hash-based joins and Pandas vectorization to efficiently process 150k+ records. |
| Databases | MongoDB Optimization & Analytics | Designed aggregation pipelines to analyze shelter outcomes and trends, implemented compound indexes for performance, and deployed securely using MongoDB Atlas. |
The Grazioso Salvare dataset does not include geographic coordinate data (latitude and longitude). Location information is provided only as unstructured text fields (e.g., “Found Location”), which limits the ability to perform true geospatial plotting without introducing external services.
Rather than relying on third-party geocoding APIs—which introduce rate limits, accuracy concerns due to inconsistent address formatting, and external dependencies—the dashboard map was intentionally designed to center on the Austin, TX service region and provide contextual location awareness without fabricating or over-processing data.
This design decision reflects real-world data engineering constraints and emphasizes responsible handling of incomplete datasets while maintaining transparency and system reliability.
This ePortfolio highlights skills essential for entry-level data engineering roles, including building scalable ETL pipelines, optimizing data transformations, and designing secure, cloud-based data systems. The artifact reflects my readiness to contribute to real-world data engineering teams and supports my long-term goal of advancing into data systems engineering and leadership roles in technology.
Additional materials: