Data/Analytics/ML Project Architecture Questions

Project Type and Scope

  1. Primary project focus:

  2. Scale of data:

  3. Data velocity:

Programming Language and Environment

  1. Primary language:

  2. Development environment:

  3. Transition from notebooks to production:

Data Sources

  1. Data source types:

  2. Data ingestion frequency:

  3. Data ingestion tools:

Data Storage

  1. Primary data storage:

  2. Storage format (for files):

  3. Data partitioning strategy:

  4. Data retention policy:

Data Processing and Transformation

  1. Data processing framework:

  2. Compute platform:

  3. ETL tool (if applicable):

  4. Data quality checks:

  5. Schema management:

Machine Learning (if applicable)

  1. ML framework:

  2. ML use case:

  3. Model training infrastructure:

  4. Experiment tracking:

  5. Model registry:

  6. Feature store:

  7. Hyperparameter tuning:

  8. Model serving (inference):

  9. Model serving platform (if real-time):

  10. Model monitoring (in production):

  11. AutoML tools:

Orchestration and Workflow

  1. Workflow orchestration:

  2. Orchestration platform:

  3. Job scheduling:

  4. Dependency management:

Data Analytics and Visualization

  1. BI/Visualization tool:

  2. Reporting frequency:

  3. Query interface:

Data Governance and Security

  1. Data catalog:

  2. Data lineage tracking:

  3. Access control:

  4. PII/Sensitive data handling:

  5. Data versioning:

Testing and Validation

  1. Data testing:

  2. ML model testing (if applicable):

Deployment and CI/CD

  1. Deployment strategy:

  2. Environment separation:

  3. Containerization:

Monitoring and Observability

  1. Pipeline monitoring:

  2. Performance monitoring:

  3. Alerting:

Cost Optimization

  1. Cost considerations:

Collaboration and Documentation

  1. Team collaboration:

  2. Documentation approach:

  3. Code review process:

Performance and Scale

  1. Performance requirements:

  2. Scalability needs:

  3. Query optimization: