Prof-Drake-UMD/INST767-Sp25
Mar 2025 – May 2025
2 Months active
Languages Used
MarkdownCSVDockerfileGit ConfigurationJSONPythonSQLShell
Technical Skills
DocumentationAPI DevelopmentAPI IntegrationAPI Integration (Removal)Backend DevelopmentBigQuery
Andrea Murano
PROFILE
Andrea Murano
Andrea Murano developed a robust data ingestion and export pipeline for the Prof-Drake-UMD/INST767-Sp25 repository, focusing on cultural data aggregation from sources like Spotify, Open Library, and the MET. Leveraging Python, SQL, and Docker, Andrea designed modular scripts for data retrieval, transformation, and CSV export, while implementing scalable orchestration and containerized deployments. The work included refactoring the API logic layer, enhancing database schema management, and improving documentation to streamline onboarding and maintenance. Through extensive code cleanup, dependency management, and environment configuration, Andrea delivered a maintainable, production-ready backend that supports analytics and reliable data processing across multiple content domains.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
205Total
Bugs
9
Commits
205
Features
69
Lines of code
8,201
Activity Months2
Your Network
19 people
Work History
May 2025
201 Commits • 68 Features
May 1, 2025May 2025 monthly highlights for Prof-Drake-UMD/INST767-Sp25: Delivered a foundational data ingestion, retrieval, and export stack with strong documentation, scalable pipeline entry points, and robust tooling. Key features shipped include substantial documentation improvements (README across multiple commits), initial project scaffolding, and a cultural schema foundation with cultural_table_schema.sql. Implemented data retrieval and CSV export capabilities for books, art, and music (get_books.py, get_art.py, get_music.py; write_csv.py) and built the cultural_experience items data model with corresponding build and orchestration utilities. Established core data pipeline entry points (transform main.py and ingest main.py) and updated the main orchestration to improve reliability. API logic enhancements and a refactored API layer strengthened data handling and request processing. Packaging and environment setup were hardened with updated requirements.txt and a Dockerfile to enable reproducible deployments. Added data ingestion scripts (ingest_spotify.py, ingest_openlibrary.py, ingest_met.py) and restructured DAG/pipeline paths for future scalability. Core database work included create_tables.sql updates and queries improvements. All together, these efforts positioned the project for production reliability, improved analytics readiness, and a cleaner, more scalable codebase.
201 Commits • 68 Features
May 1, 2025May 2025 monthly highlights for Prof-Drake-UMD/INST767-Sp25: Delivered a foundational data ingestion, retrieval, and export stack with strong documentation, scalable pipeline entry points, and robust tooling. Key features shipped include substantial documentation improvements (README across multiple commits), initial project scaffolding, and a cultural schema foundation with cultural_table_schema.sql. Implemented data retrieval and CSV export capabilities for books, art, and music (get_books.py, get_art.py, get_music.py; write_csv.py) and built the cultural_experience items data model with corresponding build and orchestration utilities. Established core data pipeline entry points (transform main.py and ingest main.py) and updated the main orchestration to improve reliability. API logic enhancements and a refactored API layer strengthened data handling and request processing. Packaging and environment setup were hardened with updated requirements.txt and a Dockerfile to enable reproducible deployments. Added data ingestion scripts (ingest_spotify.py, ingest_openlibrary.py, ingest_met.py) and restructured DAG/pipeline paths for future scalability. Core database work included create_tables.sql updates and queries improvements. All together, these efforts positioned the project for production reliability, improved analytics readiness, and a cleaner, more scalable codebase.
May 2025
March 2025
4 Commits • 1 Features
Mar 1, 2025March 2025: Delivered a comprehensive README update for the American't: Finding a New Home in Europe! project, consolidating purpose, data flows, and API usage into a single, developer-friendly reference. The documentation clarifies how Eurostat, Google Places, and Geonames APIs feed the city-recommendation logic, improving onboarding, transparency, and maintainability.
March 2025
4 Commits • 1 Features
Mar 1, 2025March 2025: Delivered a comprehensive README update for the American't: Finding a New Home in Europe! project, consolidating purpose, data flows, and API usage into a single, developer-friendly reference. The documentation clarifies how Eurostat, Google Places, and Geonames APIs feed the city-recommendation logic, improving onboarding, transparency, and maintainability.
Activity
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Quality Metrics
Correctness86.2%
Maintainability86.2%
Architecture83.0%
Performance80.6%
AI Usage21.4%
Skills & Technologies
Programming Languages
CSVDockerfileGit ConfigurationJSONMarkdownPythonSQLShellTexttext
Technical Skills
API DevelopmentAPI IntegrationAPI Integration (Removal)Backend DevelopmentBigQueryCSV ExportCSV GenerationCloudCloud ComputingCloud FunctionsCloud Pub/SubCloud ServicesCloud Services (GCP)Code CleanupCode Commenting
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