Data Warehouse Concepts, Design, and Data Integration course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Introduction to Data Warehouse Architecture
Estimated time: 6 hours
- Understand the purpose and role of data warehouses in enterprise environments
- Distinguish between OLTP and OLAP systems
- Explore enterprise data integration concepts
- Study high-level data warehouse architecture components
Module 2: Dimensional Modeling Fundamentals
Estimated time: 6 hours
- Identify and define fact tables and dimension tables
- Design star schema models for analytical reporting
- Apply surrogate keys in dimension tables
- Incorporate hierarchies into dimensional models
Module 3: Star and Snowflake Schema Design
Estimated time: 6 hours
- Compare star and snowflake schema structures
- Implement normalization strategies in snowflake schemas
- Translate business metrics into logical data models
- Evaluate schema trade-offs for query performance
Module 4: Advanced Schema Design and Optimization
Estimated time: 7 hours
- Apply indexing strategies to improve query efficiency
- Implement slowly changing dimensions (SCD) Type 1 and Type 2
- Use aggregation and partitioning techniques for scalability
- Optimize schemas for large-scale reporting workloads
Module 5: Practical Design Application
Estimated time: 7 hours
- Convert business requirements into dimensional models
- Build end-to-end data warehouse schema designs
- Validate schema integrity and performance
Module 6: Final Project
Estimated time: 8 hours
- Design a complete data warehouse solution for a real-world scenario
- Present a star schema model with fact and dimension tables
- Justify design decisions based on reporting and scalability needs
Prerequisites
- Familiarity with SQL fundamentals
- Basic understanding of relational databases
- Exposure to data management concepts
What You'll Be Able to Do After
- Design scalable data warehouse architectures
- Create efficient star and snowflake schemas
- Model dimensional data from business requirements
- Optimize data models for analytics and reporting
- Apply best practices in enterprise data integration