Ripotek Technologies Inc.
Design. Engineer. Deliver.
Calgary, Alberta
www.ripotek.com
training@ripotek.com
Azure Data Engineer
Professional Training Program Syllabus
Program Overview
24 Weeks (3 sessions per week)
72 total sessions, 216 instructional hours
CAD $1,500
Flexible payment plans available
Tuesday/Thursday/Sunday
6:00 PM - 9:00 PM Mountain Time
Microsoft DP-203
Azure Data Engineer Associate
Program Description
The Azure Data Engineer program is an intensive 24-week training designed to transform you into a cloud data engineering professional. This comprehensive program covers the full spectrum of Azure data services including Azure Data Factory, Synapse Analytics, Databricks, Data Lake Storage, and modern data lakehouse architectures.
Through hands-on projects and real-world scenarios, you'll master ELT pipeline design, data modeling, performance optimization, DevOps practices, and security implementation. By completion, you'll have built a portfolio of enterprise-grade solutions and be fully prepared for the Microsoft DP-203 certification exam.
Prerequisites
Required
- SQL proficiency (queries, joins, aggregations)
- Basic Python or another programming language
- Understanding of data concepts (databases, ETL)
- Familiarity with cloud computing concepts
- Azure subscription (provided for labs)
Recommended
- Experience with data warehousing or BI
- Azure Fundamentals (AZ-900) knowledge
- Git and version control familiarity
- JSON and data formats knowledge
Learning Outcomes
Upon successful completion of this program, you will be able to:
Curriculum Overview: 5 Phases, 24 Weeks
Phase 1: Foundations (Weeks 1-5)
Azure fundamentals, SQL, and data storage
Week 1: Azure Data Platform Overview
- Azure data services ecosystem
- Resource groups, subscriptions, and management
- Storage accounts and data lake fundamentals
- Security and access control basics
Week 2: Advanced SQL for Data Engineering
- Complex joins and subqueries
- Window functions and CTEs
- Performance tuning and indexing
- Stored procedures and functions
Week 3: Azure Data Lake Storage Gen2
- Hierarchical namespace and file systems
- Access tiers and lifecycle management
- Security with RBAC and ACLs
- Best practices for folder structure
Week 4: Azure SQL Database
- Deployment models and service tiers
- Elastic pools and scaling
- Security features and encryption
- Backup and disaster recovery
Week 5: Cosmos DB for Big Data
- NoSQL concepts and APIs
- Partition keys and throughput
- Change feed and analytical store
- Integration with data platforms
Phase 1 Project:
Design and implement multi-tier data storage solution
Phase 2: Data Integration (Weeks 6-11)
Azure Data Factory and pipeline engineering
Week 6: Azure Data Factory Fundamentals
- ADF architecture and components
- Linked services and datasets
- Copy activity and data movement
- Integration runtimes
Week 7: Pipeline Orchestration
- Control flow activities
- Parameters and variables
- Conditional logic and loops
- Pipeline dependencies and triggers
Week 8: Data Transformation
- Mapping data flows
- Transformations and derived columns
- Aggregations and pivoting
- Data flow debugging
Week 9: Advanced ADF Patterns
- Metadata-driven pipelines
- Dynamic pipeline execution
- Error handling and retry logic
- Logging and monitoring
Week 10: Azure Databricks Integration
- Databricks workspace and clusters
- Notebook activities in ADF
- Passing parameters to notebooks
- Mounting data lakes
Week 11: Incremental Loading Patterns
- Change data capture (CDC)
- Watermark-based loading
- Delta lake merge operations
- Performance optimization
Phase 2 Project:
Build end-to-end ELT pipeline for enterprise data warehouse
Phase 3: Analytics and Data Warehousing (Weeks 12-16)
Azure Synapse Analytics and dimensional modeling
Week 12: Azure Synapse Analytics Overview
- Synapse workspace and components
- Dedicated SQL pools vs serverless
- Spark pools and notebooks
- Integration with Power BI
Week 13: Dedicated SQL Pools
- Distribution strategies (hash, round-robin, replicated)
- Table design and partitioning
- Columnstore indexes
- Query optimization techniques
Week 14: Serverless SQL Pools
- Querying data lake files
- External tables and views
- OPENROWSET function
- Cost optimization strategies
Week 15: Dimensional Modeling
- Star schema vs snowflake schema
- Fact and dimension table design
- Slowly changing dimensions (SCD)
- Surrogate keys and business keys
Week 16: Performance Tuning
- Query execution plans
- Statistics and data skew
- Resource classes and workload management
- Materialized views and result set caching
Phase 3 Project:
Build optimized data warehouse with star schema
Phase 4: Advanced Topics (Weeks 17-21)
Streaming, security, and DevOps
Week 17: Stream Processing
- Azure Event Hubs fundamentals
- Stream Analytics jobs
- Windowing functions
- Real-time dashboards
Week 18: Data Security and Compliance
- Azure Active Directory integration
- Managed identities and service principals
- Data encryption (at rest and in transit)
- Auditing and compliance features
Week 19: DevOps for Data Engineering
- Git integration with ADF
- CI/CD pipelines with Azure DevOps
- Environment management (Dev, Test, Prod)
- Automated testing strategies
Week 20: Monitoring and Troubleshooting
- Azure Monitor and Log Analytics
- Alerts and notifications
- Pipeline debugging techniques
- Performance diagnostics
Week 21: Cost Optimization
- Azure Cost Management tools
- Resource sizing and scaling
- Reserved capacity and savings plans
- Best practices for cost control
Phase 4 Project:
Implement secure, production-ready data platform with CI/CD
Phase 5: Capstone and Certification (Weeks 22-24)
Final project and DP-203 exam preparation
Week 22: Capstone Project - Planning and Design
- Project requirements gathering
- Architecture design and documentation
- Technology stack selection
- Implementation kickoff
Week 23: Capstone Project - Implementation
- Data ingestion and transformation
- Data warehouse development
- Security and monitoring implementation
- Testing and validation
Week 24: DP-203 Exam Prep and Presentations
- DP-203 exam structure and topics
- Practice questions and mock exams
- Capstone project presentations
- Career guidance and next steps
Capstone Project:
Enterprise data lakehouse with full ELT pipeline, data warehouse, and analytics
Assessment and Grading
| Assessment Component | Weight | Description |
|---|---|---|
| Phase Projects (5) | 40% | One project per phase |
| Weekly Labs | 20% | Hands-on exercises and assignments |
| Participation | 10% | Class engagement and discussions |
| Capstone Project | 30% | Comprehensive final project |
Grading Scale
A minimum grade of 70% is required to receive a certificate of completion
Materials and Resources
Required Tools
- Azure SubscriptionProvided for lab exercises
- Azure Data StudioFree database tool
- Visual Studio CodeWith Azure extensions
- GitVersion control system
Provided Resources
- Course MaterialsSlides, code samples, datasets
- Azure Credits$500 per student for labs
- Exam VoucherMicrosoft DP-203 certification
- Practice TestsMeasureUp practice exams
Career Services and Job Placement
Azure Data Engineers are in high demand across Energy, Finance, Healthcare, and Technology sectors. Our graduates secure roles at leading organizations within 90 days on average.
Career Support Includes:
- Dedicated career coaching
- Resume and portfolio development
- Technical interview preparation
- Direct introductions to hiring partners
- LinkedIn optimization and networking
Typical Job Titles:
- Azure Data Engineer
- Data Platform Engineer
- Cloud Data Engineer
- ETL/ELT Developer
- Data Warehouse Architect