- Normalized schema design
- Complex relationships
- Index optimization
- Query performance
- Version control migrations
- AI-powered schema design optimization
- Intelligent query optimization suggestions
- Relational model & ACID
- Normalization (3NF/BCNF)
- Query optimization
- Transaction management
- Migration strategies
- AI for database design patterns
- Automated data modeling assistance
- ERD documentation
- Migration scripts in Git
- Performance benchmarks
- Data model rationale
- PostgreSQL/MySQL
- DBeaver
- Flyway/Liquibase
- Draw.io
- AI-powered ERD generators
- Technical design reviews
- Sprint 5-6 execution
- DB change management
- PR reviews for schemas
- Unit test suite
- Integration tests
- Load testing scripts
- Test data generation
- 80%+ code coverage
- AI-generated test cases and scenarios
- Automated edge case identification
- Testing pyramid
- TDD principles
- Test doubles
- Performance metrics
- Continuous testing
- AI for test automation patterns
- Smart test case prioritization
- Test coverage reports
- Load test results
- Test plan document
- CI pipeline with tests
- JUnit/Mockito
- REST Assured
- JMeter
- SonarQube
- AI-powered testing tools
- QA integration
- Bug tracking
- Sprint 6 testing focus
- Test-driven development
- Dockerize application
- Terraform scripts
- GCP deployment
- CI/CD pipeline
- GitOps practices
- Complete Anthropic's "Building with Claude API" modules
- AI deployment considerations and best practices
- Cloud service models
- Container orchestration
- Infrastructure as Code
- CI/CD principles
- Cloud security
- AI service integration patterns
- Responsible AI deployment practices
- Docker images
- Terraform modules in Git
- CI/CD configuration
- Deployment docs
- Google Cloud
- Terraform
- Docker
- GitHub Actions
- Claude API
- AI model deployment platforms
- Release management
- Environment strategy
- Sprint 7-8 DevOps
- GitOps workflows
- Data warehouse design
- ETL pipelines
- Fact/dimension tables
- Analytics dashboards
- Incremental loads
- AI-powered ETL pipeline optimization
- Intelligent data quality analysis
- OLTP vs OLAP
- Dimensional modeling
- ETL/ELT patterns
- Data lineage
- Business intelligence
- AI-enhanced data analysis patterns
- Automated anomaly detection in data
- DW schema
- ETL pipeline code
- Analytics dashboards
- Data dictionary
- BigQuery
- Apache Airflow
- Looker Studio
- Python ETL
- AI-powered data transformation tools
- Data governance
- Sprint 9-10 planning
- Analytics requirements
- Data quality metrics
- Authentication/authorization
- Input validation
- Firewall configuration
- Secret management
- Security scanning
- AI security risk assessment
- Responsible AI implementation practices
- OWASP Top 10
- Secure coding
- Zero trust architecture
- Cloud security
- Threat modeling
- AI system security considerations
- Data privacy in AI applications
- Security audit report
- Hardening checklist
- IAM documentation
- Vulnerability scans
- Spring Security
- GCP IAM
- OWASP ZAP
- Secret Manager
- AI security assessment tools
- Security in SDLC
- Sprint 11 security
- Penetration testing
- Incident response
- Deploy monitoring agents
- Centralized logging
- Create dashboards
- Implement alerting
- Elasticsearch integration
- AI-powered log analysis and insights
- Intelligent anomaly detection systems
- Observability pillars
- SRE principles
- Alert fatigue
- Search architecture
- Distributed tracing
- AI for predictive monitoring
- Smart alerting and noise reduction
- Monitoring dashboards
- Alert runbooks
- Search implementation
- Observability guide
- Elastic Stack
- Prometheus/Grafana
- GCP Operations
- Jaeger
- AI-powered monitoring platforms
- Incident management
- Post-mortem process
- Sprint 12-13 execution
- On-call basics
- Code refactoring techniques
- Performance optimization
- MERN stack architecture
- Technical debt management
- Microservices patterns
- Advanced AI integration patterns
- AI-powered development tools
- Design patterns (Creational, Structural, Behavioral)
- MERN stack & three-tier architecture
- Technical debt identification & management
- Database design patterns (Sharding, Partitioning)
- Microservices with Node.js & Docker
- AI service architecture patterns
- Scalable AI system design
- Refactored codebase with patterns
- Performance optimization report
- Technical debt analysis
- Architecture design documentation
- Python, Node.js, React.js
- MERN Stack (MongoDB, Express, React, Node)
- PostgreSQL, MySQL, SQLite
- Docker, RabbitMQ
- AI-powered development tools
- Code review best practices
- Refactoring methodologies
- Performance benchmarking
- Technical debt prioritization
- Infrastructure: Kubernetes, Advanced networking
- Full-Stack: Microservices, GraphQL
- Data: Spark, ML pipelines
- Complete Google's "Advanced: Generative AI for Developers Learning Path"
- Complete Google's "Machine Learning Crash Course"
- Advanced concepts
- Certification prep
- Industry standards
- Best practices
- Advanced AI/ML specialization tracks
- Enterprise AI implementation patterns
- Specialization project
- Certification attempt
- Advanced portfolio piece
- Track-specific
- Advanced platforms
- Enterprise tools
- TensorFlow/PyTorch
- Google Cloud AI Platform
- Individual planning
- Certification strategy
- Career path guidance
- Communication workshops
- Presentation skills
- Client interaction
- Mock interviews
- Team collaboration
- AI-assisted professional communication
- AI for technical documentation
- Active listening
- Empathy building
- Conflict resolution
- Task management
- Stress management
- AI collaboration best practices
- Professional AI tool usage ethics
- Professional portfolio
- LinkedIn profile
- Mock interview results
- EQ development plan
- Zoom/Meet
- Google Slides
- LinkedIn
- Miro
- AI writing assistants
- Professional AI communication tools
- Agile ceremonies
- Client protocols
- Professional ethics
- Career planning