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Duration 14 hours
Course Outline
Overview of Data Warehousing
- Definition and purpose of a data warehouse
- Advantages of warehousing for analytics and reporting
- Support for warehousing in Oracle Database 19c
Architecture of Oracle Data Warehouses
- Core components: source data, ETL, staging, and presentation layers
- Comparison of star and snowflake schemas
- Oracle tools for managing data warehouse environments
Data Modeling Principles
- Structure of fact and dimension tables
- Concepts of surrogate keys and granularity
- Fundamentals of slowly changing dimensions (SCD)
Introduction to ETL Workflows
- Overview of ETL processes and Oracle-supported tools
- Distinction between batch and real-time loading
- Common challenges in data integration and quality assurance
Querying and Reporting Principles
- Differences between OLAP and OLTP workloads
- Methods Oracle uses to optimize queries for data warehouses
- Introduction to materialized views and aggregate structures
Planning and Scaling Oracle Warehouses
- Considerations for hardware and architecture
- Benefits of partitioning and compression
- Overview of Oracle licensing and features
Practical Applications and Best Practices
- Case studies on warehouse design
- Best practices for planning Oracle data warehouse projects
- Steps for initiating a pilot implementation
Recap and Future Steps
Requirements
- Familiarity with relational databases
- Foundational knowledge of SQL
- No previous experience with Oracle data warehousing is necessary
Target Audience
- Data analysts
- IT personnel intending to work with Oracle data warehousing
- Business intelligence teams
Testimonials (1)
good explanation on each points and provide assignment for practices.
