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Duration 14 hours
Course Outline
Overview of Data Warehousing
- Defining the data warehouse
- Advantages of warehousing for analytics and reporting
- Warehousing capabilities in Oracle Database 19c
Oracle Data Warehouse Architecture
- Core components: source data, ETL, staging, and presentation layers
- Comparison of Star and Snowflake schemas
- Oracle tools for managing Data Warehouse (DW) environments
Data Modeling Principles
- Fact and dimension tables
- Understanding surrogate keys and granularity
- Basics of Slowly Changing Dimensions (SCD)
Introduction to ETL Processes
- Overview of ETL and relevant Oracle-supported tools
- Batch loading versus real-time loading strategies
- Addressing challenges in data integration and quality assurance
Query and Reporting Principles
- Differences between OLAP and OLTP workloads
- Methods Oracle uses to optimize data warehouse queries
- Introduction to materialized views and aggregate tables
Planning and Scaling Oracle Warehouses
- Considerations for hardware and architectural design
- Benefits of partitioning and compression techniques
- Overview of Oracle licensing and relevant features
Use Cases and Best Practices
- Review of warehouse design case studies
- Best practices for planning Oracle DW projects
- Initiating a pilot implementation
Conclusion and Future Directions
Requirements
- Familiarity with relational database concepts
- Foundational knowledge of SQL
- Prior experience with Oracle data warehousing is not mandatory
Target Audience
- Data analysts
- IT professionals preparing to engage with Oracle data warehousing solutions
- Business intelligence teams
Testimonials (1)
good explanation on each points and provide assignment for practices.
