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Course Outline
Introduction to Data Warehousing
- Defining what a data warehouse is
- Advantages of warehousing for analytics and reporting
- How Oracle Database 19c supports warehousing
Architecture of Oracle Data Warehouses
- Essential components: source data, ETL, staging areas, and presentation layers
- Comparing star and snowflake schemas
- Oracle tools for managing data warehouse environments
Concepts in Data Modeling
- Understanding fact and dimension tables
- Working with surrogate keys and determining granularity
- Fundamentals of Slowly Changing Dimensions (SCD)
Introduction to ETL Processes
- Overview of ETL workflows and Oracle-supported tools
- Distinctions between batch and real-time data loading
- Addressing challenges related to data integration and quality
Concepts in Querying and Reporting
- Fundamental differences between OLAP and OLTP workloads
- Mechanisms Oracle employs to optimize queries for data warehouses
- Overview of materialized views and aggregation techniques
Planning and Scaling Oracle Warehouses
- Considerations for hardware and architecture
- Advantages of partitioning and data compression
- Overview of Oracle licensing and feature sets
Use Cases and Best Practices
- Case studies on warehouse design
- Best practices for planning Oracle data warehouse projects
- Initiating a pilot implementation
Summary and Next 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 utilize Oracle data warehousing solutions
- Business intelligence teams
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
