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Duration 14 hours
Course Outline
Foundations of Data Warehousing
- Defining the concept of a data warehouse
- Advantages of warehousing for analytics and reporting workflows
- Support provided by Oracle Database 19c for warehousing needs
Structure of Oracle Data Warehouses
- Essential elements: source data, ETL processes, staging areas, and presentation layers
- Comparison between star and snowflake schemas
- Oracle utilities used for administering DW environments
Principles of Data Modeling
- The role of fact and dimension tables
- Understanding surrogate keys and data granularity
- Basic concepts of slowly changing dimensions (SCD)
Overview of ETL Workflows
- Introduction to ETL and Oracle-integrated tools
- Differences between batch and real-time data loading
- Common hurdles in data integration and quality assurance
Querying and Reporting Principles
- Distinguishing between OLAP and OLTP workloads
- Methods Oracle uses to optimize warehouse queries
- Introduction to materialized views and data aggregation
Strategic Planning and Scalability of Oracle Warehouses
- Considerations regarding hardware and architectural design
- Benefits derived from partitioning and compression techniques
- Overview of Oracle licensing and feature sets
Practical Applications and Recommended Practices
- Examination of warehouse design case studies
- Recommended practices for initiating Oracle DW projects
- Steps for launching a pilot implementation
Conclusion and Future Directions
Requirements
- Familiarity with relational database structures
- Foundational understanding of SQL
- No previous experience with Oracle data warehousing is necessary
Target Audience
- Data analysts
- IT professionals intending to engage with Oracle warehousing systems
- Business intelligence teams
Testimonials (1)
good explanation on each points and provide assignment for practices.
