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Course Outline
Foundations of Data Warehousing
- Warehouse purpose, components, and architecture.
- Data marts, enterprise warehouses, and lakehouse patterns.
- OLTP vs OLAP fundamentals and workload separation.
Dimensional Modeling
- Facts, dimensions, and grain.
- Star schema vs snowflake schema.
- Slowly Changing Dimensions types and handling.
ETL and ELT Processes
- Extraction strategies from OLTP 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.
Audience
- Data analysts moving 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
