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Course Outline
Foundations of Data Warehousing
- Defining a data warehouse
- The advantages of warehousing for analytics and reporting
- How Oracle Database 19c supports warehousing requirements
Oracle Data Warehouse Architectural Design
- Core elements: source data, ETL processes, staging, and presentation layers
- Comparison between star and snowflake schemas
- Oracle tools available for managing DW environments
Data Modeling Principles
- The role of fact and dimension tables
- Understanding surrogate keys and granularity
- Basic concepts of slowly changing dimensions (SCD)
Overview of ETL Processes
- General ETL workflows and Oracle-supported tooling
- Distinctions between batch and real-time data loading
- Addressing challenges in data integration and quality
Querying and Reporting Fundamentals
- Understanding the difference between OLAP and OLTP workloads
- Methods Oracle uses to optimize queries for data warehouses
- Introduction to materialized views and aggregation
Planning and Scaling Oracle Warehouses
- Considerations for hardware and architecture
- The benefits of partitioning and data compression
- An overview of Oracle licensing and features
Real-World Applications and Best Practices
- Case studies on warehouse design
- Best practices for planning Oracle DW projects
- Steps to initiate a pilot implementation
Recap and Future Directions
Requirements
- Familiarity with relational database systems
- Foundational knowledge of SQL
- No previous experience with Oracle data warehousing is necessary
Target Audience
- Data analysts
- IT personnel intending to work with Oracle data warehousing
- Business intelligence teams
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.