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  1. System Architecture Design 1.1. Define the overall system architecture 1.2. Identify key components: Neo4j database, React frontend, Flask backend, SQL user database 1.3. Plan the communication flow between components

  2. Neo4j Database Setup 2.1. Install Neo4j on the server 2.2. Design the graph schema for data lineage tracking 2.3. Create necessary node types (e.g., DataSource, ETLProcess, MLModel, Output) 2.4. Define relationships between nodes (e.g., INGESTED_BY, TRANSFORMED_BY, USED_IN)

  3. Flask Backend Development 3.1. Set up a Flask project structure 3.2. Create necessary routes for data ingestion, processing, and retrieval 3.3. Implement authentication and authorization middleware 3.4. Develop Neo4j connection and query functions 3.5. Create API endpoints for frontend communication

  4. SQL User Database Setup 4.1. Choose and install a SQL database (e.g., PostgreSQL) 4.2. Design the user schema (e.g., id, username, hashed_password, role) 4.3. Implement encryption for sensitive user data 4.4. Create database migration scripts

  5. User Authentication and Authorization 5.1. Implement user registration functionality 5.2. Develop login and logout routes 5.3. Create JWT token generation and validation 5.4. Implement role-based access control

  6. Data Ingestion Layer 6.1. Develop connectors for various data sources 6.2. Implement data validation and cleaning processes 6.3. Create Neo4j nodes for each ingested data source 6.4. Store metadata about the ingestion process

  7. ETL Process Tracking 7.1. Develop a framework to wrap ETL processes 7.2. Implement logging for each transformation step 7.3. Create Neo4j nodes for ETL processes 7.4. Establish relationships between data sources and ETL processes

  8. Machine Learning Model Integration 8.1. Develop a system to track ML model training and deployment 8.2. Create Neo4j nodes for ML models 8.3. Establish relationships between data, ETL processes, and ML models 8.4. Implement versioning for ML models

  9. Output Tracking 9.1. Develop a system to track model outputs and predictions 9.2. Create Neo4j nodes for outputs 9.3. Establish relationships between models and outputs

  10. Model Evaluation Metrics Implementation 10.1. Define a set of evaluation metrics for different types of models (e.g., classification, regression, clustering) 10.2. Implement calculation functions for each metric (e.g., accuracy, precision, recall, F1-score, RMSE, R-squared) 10.3. Create a framework to automatically calculate and store metrics for each model version 10.4. Develop a system to track metric changes over time and across model versions 10.5. Implement threshold alerts for metric degradation 10.6. Create Neo4j nodes for evaluation metrics and link them to corresponding model versions 10.7. Develop visualization components for metric trends and comparisons

  11. React Frontend Development 11.1. Set up a new React project 11.2. Design the user interface for data lineage visualization 11.3. Implement user authentication and authorization in the frontend 11.4. Create components for displaying data sources, ETL processes, ML models, and outputs 11.5. Develop an interactive graph visualization for data lineage 11.6. Create dashboards for model performance metrics

  12. API Integration 12.1. Implement API calls from React to Flask backend 12.2. Handle data fetching and state management (e.g., using Redux) 12.3. Implement real-time updates using WebSockets or polling

  13. Data Lineage Visualization 13.1. Integrate a graph visualization library (e.g., D3.js or vis.js) 13.2. Develop interactive features for exploring the data lineage graph 13.3. Implement filtering and search functionality for the graph

  14. Reporting and Export Features 14.1. Develop report generation functionality 14.2. Implement data export options (e.g., CSV, JSON) 14.3. Create visualizations for data provenance statistics 14.4. Develop model performance reports including evaluation metrics

  15. Testing 15.1. Develop unit tests for backend functions 15.2. Create integration tests for the entire system 15.3. Perform user acceptance testing 15.4. Conduct security and penetration testing 15.5. Implement tests for metric calculation accuracy

  16. Documentation 16.1. Create user manuals for the system 16.2. Develop API documentation 16.3. Write developer documentation for future maintenance 16.4. Document the evaluation metrics used and their interpretations

  17. Deployment 17.1. Set up production servers for Neo4j, Flask, and SQL databases 17.2. Configure a web server (e.g., Nginx) for the Flask application 17.3. Set up a build pipeline for the React frontend 17.4. Implement monitoring and logging solutions

  18. Performance Optimization 18.1. Optimize Neo4j queries for large-scale data 18.2. Implement caching mechanisms for frequently accessed data 18.3. Optimize React rendering for large graphs 18.4. Optimize metric calculation for large datasets

  19. Compliance and Auditing 19.1. Implement audit logging for all system actions 19.2. Develop compliance reporting features 19.3. Ensure GDPR and other relevant regulatory compliance 19.4. Implement data retention policies for evaluation metrics