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Course Outline
Overview of Data Warehousing
- Defining the data warehouse
- Advantages of warehousing for analytics and reporting
- Warehousing support provided by Oracle Database 19c
Architecture of Oracle Data Warehouses
- Primary components: source data, ETL, staging, and presentation layers
- Comparing star and snowflake schemas
- Oracle tools for managing data warehouse environments
Principles of Data Modeling
- Fact and dimension tables
- Surrogate keys and data granularity
- Fundamentals of slowly changing dimensions (SCD)
Introduction to ETL Workflows
- Overview of ETL and tools supported by Oracle
- Batch processing versus real-time loading
- Challenges associated with data integration and quality
Concepts in Querying and Reporting
- Understanding OLAP and OLTP workloads
- Methods for Oracle query optimization in data warehouses
- Introduction to materialized views and aggregates
Strategic Planning and Scaling for Oracle Warehouses
- Considerations regarding hardware and architecture
- Benefits of partitioning and data compression
- Overview of Oracle licensing and features
Application Scenarios and Best Practices
- Case studies on warehouse design
- Best practices for planning Oracle DW initiatives
- Initiating a pilot implementation
Recap and Future Directions
Requirements
- Familiarity with relational database systems
- Foundational proficiency in SQL
- No previous experience with Oracle data warehousing is necessary
Target Audience
- Data analysts
- IT personnel intending to engage with Oracle data warehousing
- Business intelligence teams
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.