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Duration 14 hours (2 days)
Course Outline
Fundamentals of Data Warehousing
- Defining the data warehouse concept.
- The value of warehousing in analytics and reporting.
- Oracle Database 19c’s capabilities for warehousing.
Oracle Data Warehouse Architecture
- Core elements: source data, ETL, staging, and presentation layers.
- Comparing star and snowflake schemas.
- Oracle-specific tools for managing DW environments.
Data Modeling Principles
- Fact and dimension table structures.
- The role of surrogate keys and data granularity.
- Introduction to Slowly Changing Dimensions (SCD).
Understanding ETL Workflows
- Overview of ETL processes and Oracle-supported toolsets.
- Batch processing versus real-time data loading.
- Addressing data integration and quality challenges.
Querying and Reporting Strategies
- Distinguishing between OLAP and OLTP workloads.
- Oracle’s query optimization techniques for data warehouses.
- Getting acquainted with materialized views and aggregates.
Strategic Planning and Scalability
- Considerations for hardware and architectural design.
- Advantages of partitioning and data compression.
- Overview of Oracle licensing and available features.
Practical Applications and Best Practices
- Case studies on warehouse design.
- Recommended practices for planning Oracle DW projects.
- Steps for initiating a pilot implementation.
Key Takeaways and Future Directions
Requirements
- Familiarity with relational database systems.
- Foundational understanding of SQL.
- No previous hands-on experience with Oracle data warehousing is necessary.
Target Audience
- Data analysts.
- IT personnel preparing to engage with Oracle data warehousing solutions.
- Business intelligence teams.
Testimonials (1)
good explanation on each points and provide assignment for practices.
