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Course Outline
Introduction to Data Warehousing
- Definition and purpose of a data warehouse.
- Advantages of warehousing in analytics and reporting.
- Oracle Database 19c capabilities for warehousing.
Oracle Data Warehouse Architecture
- Key components: source data, ETL, staging areas, and presentation layers.
- Comparison of star and snowflake schemas.
- Oracle tools for managing data warehouse environments.
Data Modeling Concepts
- Understanding fact and dimension tables.
- Surrogate keys and data granularity.
- Introduction to slowly changing dimensions (SCD).
Introduction to ETL Processes
- Overview of ETL and Oracle-supported tools.
- Differences between batch and real-time data loading.
- Challenges associated with data integration and quality assurance.
Query and Reporting Concepts
- Fundamental differences between OLAP and OLTP workloads.
- How Oracle optimizes queries specifically for data warehouses.
- Introduction to materialized views and aggregate tables.
Planning and Scaling Oracle Warehouses
- Considerations for hardware and architectural design.
- Benefits 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 data warehouse projects.
- Steps for initiating a pilot implementation.
Summary and Next Steps
Requirements
- A solid understanding of relational database principles.
- Basic proficiency in SQL.
- Prior experience with Oracle data warehousing is not required.
Target Audience
- Data analysts.
- IT professionals preparing to work with Oracle data warehousing solutions.
- Business intelligence teams.
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
