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Course Outline
Foundations of Data Warehousing
- Purpose, components, and architecture of data warehouses
- Data marts, enterprise warehouses, and lakehouse patterns
- OLTP vs OLAP fundamentals and workload separation
Dimensional Modeling
- Facts, dimensions, and data grain
- Star schema vs snowflake schema
- Types and handling of Slowly Changing Dimensions
ETL and ELT Processes
- Extraction strategies from OLTP systems and APIs
- Transformations, data cleansing, and conformance
- Load patterns, orchestration, and dependency management
Data Quality and Metadata Management
- Data profiling and validation rules
- Master and reference data alignment
- Lineage, catalogs, and documentation
Analytics and Performance
- Cubing concepts, aggregates, and materialized views
- Partitioning, clustering, and indexing for analytics
- Workload management, caching, and query tuning
Security and Governance
- Access control, roles, and row-level security
- Compliance considerations and auditing
- Backup, recovery, and reliability practices
Modern Architectures
- Cloud data warehouses and elasticity
- Streaming ingestion and near real-time analytics
- Cost optimization and monitoring
Capstone: From Source to Star Schema
- Modeling a business process into facts and dimensions
- Building an end-to-end ETL or ELT workflow
- Publishing dashboards and validating metrics
Summary and Next Steps
Requirements
- Understanding of relational databases and SQL
- Experience in data analysis or reporting
- Basic familiarity with cloud or on-premises data platforms
Target Audience
- Data analysts transitioning into data warehousing
- BI developers and ETL engineers
- Data architects and team leads
35 Hours
Testimonials (1)
Hands on exercises. Class should have been 5 days, but the 3 days helped to clear up a lot of questions that I had from working with NiFi already
