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Course Outline
Fundamentals of Data Warehousing
- Defining the data warehouse concept
- Advantages of warehousing for analytics and reporting
- Warehousing capabilities in Oracle Database 19c
Oracle Data Warehouse Architecture
- Core components: source data, ETL processes, staging areas, and presentation layers
- Comparing star and snowflake schema designs
- Oracle tools used to manage data warehouse environments
Data Modeling Principles
- Structure of fact and dimension tables
- Understanding surrogate keys and data granularity
- Introduction to Slowly Changing Dimensions (SCD)
Overview of ETL Processes
- Introduction to ETL and Oracle-supported tools
- Distinction between batch and real-time data loading
- Addressing challenges in data integration and quality assurance
Querying and Reporting Strategies
- Differentiating OLAP and OLTP workloads
- How Oracle optimizes queries for warehouse performance
- Introduction to materialized views and aggregate tables
Planning and Scaling Oracle Warehouses
- Considerations for hardware and system architecture
- Benefits of partitioning and data compression
- Overview of Oracle licensing and available features
Real-World Applications and Best Practices
- Analysis of warehouse design case studies
- Recommended best practices for planning Oracle DW initiatives
- Steps to initiate a pilot implementation
Recap and Future Directions
Requirements
- Familiarity with relational database systems
- Fundamental proficiency in SQL
- No previous experience with Oracle data warehousing is necessary
Intended Audience
- Data analysts
- IT professionals preparing to engage with Oracle data warehousing solutions
- Business intelligence teams
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
