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Duration 14 hours
Course Outline
Overview of Data Warehousing
- Definition and purpose of a data warehouse.
- Advantages of warehousing in analytics and reporting.
- How Oracle Database 19c supports warehousing needs.
Architecture of Oracle Data Warehouses
- Core elements: source data, ETL, staging areas, and presentation layers.
- Comparison between star and snowflake schema designs.
- Oracle utilities for managing data warehouse environments.
Principles of Data Modeling
- Structure and function of fact and dimension tables.
- Concepts of surrogate keys and data granularity.
- Introduction to slowly changing dimensions (SCD).
Fundamentals of ETL Processes
- General overview of ETL and supported Oracle tools.
- Distinction between batch and real-time data loading.
- Common challenges in data integration and quality management.
Concepts in Querying and Reporting
- Differences between OLAP and OLTP workloads.
- Methods Oracle uses to optimize queries in data warehouse contexts.
- Introduction to materialized views and data aggregation.
Strategy for Planning and Scaling Oracle Warehouses
- Considerations for hardware and architectural design.
- Benefits of implementing partitioning and compression.
- Overview of Oracle licensing and available features.
Practical Applications and Recommended Practices
- Analysis of warehouse design case studies.
- Best practices for structuring Oracle DW projects.
- Steps to initiate a pilot implementation.
Recap and Future Directions
Requirements
- Familiarity with relational database systems.
- Fundamental proficiency in SQL.
- No previous experience with Oracle data warehousing is necessary.
Target Audience
- Data analysts.
- IT personnel preparing to manage Oracle data warehousing environments.
- Business intelligence teams.
Testimonials (1)
good explanation on each points and provide assignment for practices.