Project Guidelines and Requirements

Minimum requirements checklist: ingestion, validation, storage, Docker, Azure deployment

Data source requirements: must use at least one external API or public dataset

Pipeline requirements: data must be validated before storage (Pydantic or equivalent)

Container requirements: Dockerfile, .env.example, runs locally with docker run

CI requirements: GitHub Actions workflow with linting and formatting checks

Deployment requirements: image in ACR, container running on Azure Container Apps

Storage requirements: results stored in Azure Postgres or ADLS

Documentation requirements: README with setup instructions and architecture overview

Stretch goals: scheduling, error reporting, multiple data sources

Submission format: GitHub repository link and screenshot of Azure deployment

Student-facing assessment rubric with concrete criteria and pass/fail thresholds

Rubric categories: working deployment, code quality, pipeline completeness, documentation, technical communication in interview

Minimum viable submission definition ("Definition of Done" checklist)

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