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Course Outline
Introduction to Data Warehousing
- Defining a data warehouse
- The benefits of warehousing for analytics and reporting
- Oracle Database 19c capabilities for warehousing
Oracle Data Warehouse Architecture
- Core components: source data, ETL processes, staging areas, and presentation layers
- Comparing star and snowflake schemas
- Oracle tools for managing data warehouse environments
Data Modeling Principles
- Understanding fact and dimension tables
- Surrogate keys and data granularity
- Fundamentals of slowly changing dimensions (SCD)
Introduction to ETL Processes
- Overview of ETL and Oracle-supported tools
- Batch loading versus real-time data ingestion
- Common challenges in data integration and quality assurance
Querying and Reporting Concepts
- Distinctions between OLAP and OLTP workloads
- How Oracle optimizes queries for data warehouse environments
- Introduction to materialized views and aggregation techniques
Planning and Scaling Oracle Warehouses
- Hardware requirements and architectural considerations
- Advantages of partitioning and data compression
- Overview of Oracle licensing and available features
Use Cases and Best Practices
- Case studies on warehouse design
- Best practices for planning Oracle DW projects
- Steps to initiate a pilot implementation
Summary and Next Steps
Requirements
- A foundational understanding of relational databases
- Basic proficiency in SQL
- No previous experience with Oracle data warehousing is required
Target Audience
- Data analysts
- IT professionals intending to work with Oracle data warehousing solutions
- Business intelligence teams
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
