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Program Overview

  • 🔗 Understanding data pipelines
  • 🦶 Data modeling basics
  • ✋ ETL vs ELT concepts
  • 🗣️ Data quality and testing
  • 🏆 Hands-on exercises
  • 🤸 Data warehouses vs data lakes
  • ⚡ SQL and NoSQL databases
  • 🧩 Batch vs streaming processing
  • 🤝 Data partitioning and optimization
  • 🏔️ Real-world examples
  • 🧗‍♂️ Building ETL pipelines with Python
  • 💪 Working with Apache Airflow
  • 🎨 Data transformation techniques
  • 🏅 Error handling and monitoring
  • 🎓 Practical implementation
  • 📊 Data validation frameworks
  • 🔍 Unit testing for data pipelines
  • 📈 Data quality metrics
  • 🚨 Monitoring and alerting
  • ✅ Best practices
  • 🚀 End-to-end data pipeline implementation
  • 📝 Best practices and patterns
  • ⚡ Performance optimization
  • 🚢 Production deployment
  • 🎉 Project completion

  • Skill Points: Students start at 0-20 points with structured guidance
  • 🤖 Bonus Tech Hour: Advanced AI and automation concepts (Skill Level 20-100)

Structured learning with step-by-step guidance and clear examples. Perfect for those new to data engineering.

  • Specific exercises assigned
  • Detailed code walkthroughs
  • Clear documentation
  • 2-3 practice problems

Guided independence with flexible project choices and multiple implementation options. Building confidence and problem-solving skills.

  • Choose between implementation approaches
  • Flexible workspace setup
  • Participate in code reviews
  • Make architectural decisions
  • Advanced exercises

Self-directed development with full implementation freedom, project design responsibilities, and mentoring opportunities.

  • Design own data pipelines
  • Configure development environments
  • Independently manage deployments
  • Lead group discussions

TimeActivityFocus
9:00-9:30am🎯 Morning SessionIntroduction and setup
9:30-10:30amHour 1: FundamentalsCore concepts and hands-on practice
10:30-11:30am💻 Hour 2: StorageData storage and processing
11:30am-12:30pmHour 3: PipelinesETL development and implementation
12:30-1:00pmLunch BreakNetworking and Q&A
1-2:00pm🔧 Hour 4: QualityTesting and monitoring
2:00-3:00pmHour 5: ProjectReal-world implementation
3:00-3:30pmReview & Next StepsProject completion and feedback
3:30-5:00pmOptional Office HoursIndividual support and mentoring
5:00-6:00pm💻 Advanced TopicsOptional advanced concepts

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