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Course Outline
Data Warehousing Foundations
- Warehouse objectives, components, and architecture.
- Data marts, enterprise warehouses, and lakehouse patterns.
- Fundamentals of OLTP versus OLAP and workload separation.
Dimensional Modeling
- Understanding facts, dimensions, and data grain.
- Comparing star and snowflake schemas.
- Managing Slowly Changing Dimensions (SCDs) types and strategies.
ETL and ELT Processes
- Strategies for extraction from OLTP systems and APIs.
- Data transformations, cleansing, and conformance.
- Load patterns, orchestration, and dependency management.
Data Quality and Metadata Management
- Data profiling and establishing validation rules.
- Aligning master and reference data.
- Managing lineage, catalogs, and documentation.
Analytics and Performance Optimization
- Concepts of cubing, aggregates, and materialized views.
- Using partitioning, clustering, and indexing for analytics.
- Workload management, caching, and query tuning.
Security and Governance
- Access control, roles, and row-level security.
- Compliance requirements and auditing practices.
- Backup, recovery, and reliability standards.
Modern Architectures
- Cloud data warehouses and elastic scalability.
- Streaming ingestion and near real-time analytics.
- Cost optimization and monitoring strategies.
Capstone Project: From Source to Star Schema
- Modeling a business process into facts and dimensions.
- Constructing an end-to-end ETL or ELT workflow.
- Publishing dashboards and validating metrics.
Summary and Next Steps
Requirements
- A solid understanding of relational databases and SQL.
- Practical experience with 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 leaders.
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
