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Duration 14 hours
Course Outline
Foundations of Data Warehousing
- Defining the data warehouse concept
- Advantages of warehousing for analytics and reporting
- Warehousing capabilities in Oracle Database 19c
Architecture of Oracle Data Warehouses
- Essential components: source data, ETL, staging, and presentation layers
- Comparing star and snowflake schema models
- Oracle utilities for managing data warehouse environments
Principles of Data Modeling
- Fact tables and dimension tables
- Understanding surrogate keys and data granularity
- Introduction to slowly changing dimensions (SCD)
Overview of ETL Workflows
- Introduction to ETL and compatible Oracle tools
- Contrasting batch and real-time data loading
- Addressing data integration and quality challenges
Querying and Reporting Fundamentals
- Differentiating OLAP and OLTP workloads
- Oracle’s approach to optimizing data warehouse queries
- Getting acquainted with materialized views and aggregate structures
Strategizing and Scaling Oracle Warehouses
- Considerations for hardware and system architecture
- The impact of partitioning and data compression
- Overview of Oracle licensing and feature sets
Practical Applications and Recommended Practices
- Analysis of warehouse design case studies
- Best practices for planning Oracle data warehouse projects
- Initiating a pilot implementation
Recap and Future Directions
Requirements
- Familiarity with relational database systems
- Foundational knowledge of SQL
- No previous background in Oracle data warehousing is necessary
Target Audience
- Data analysts
- IT personnel preparing to engage with Oracle data warehousing
- Business intelligence teams
Testimonials (1)
good explanation on each points and provide assignment for practices.
