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Course Outline
Overview of Data Warehousing
- Defining the data warehouse
- Advantages of warehousing in analytics and reporting scenarios
- Warehousing capabilities within Oracle Database 19c
Oracle Data Warehouse Structural Design
- Essential components: source data, ETL processes, staging areas, and presentation layers
- Comparison of star and snowflake schema designs
- Oracle-specific tools for administering DW environments
Principles of Data Modeling
- Structure and purpose of fact and dimension tables
- Concepts of surrogate keys and data granularity
- Introduction to Slowly Changing Dimensions (SCD)
Getting Started with ETL Workflows
- Overview of ETL methodologies and Oracle-integrated tools
- Distinctions between batch processing and real-time data loading
- Common challenges in data integration and maintaining data quality
Query Optimization and Reporting Principles
- Differentiating OLAP and OLTP workloads
- Strategies Oracle employs to optimize data warehouse queries
- Introduction to materialized views and data aggregates
Planning and Scaling Oracle Warehouses
- Considerations regarding hardware and system architecture
- Benefits of data partitioning and compression techniques
- Overview of Oracle licensing and available features
Practical Applications and Best Practices
- Analysis of warehouse design case studies
- Recommended approaches for planning Oracle DW projects
- Steps for initiating a pilot implementation
Recap and Future Directions
Requirements
- Familiarity with relational database systems
- Foundational proficiency in SQL
- Prior experience with Oracle data warehousing is not a prerequisite
Target Audience
- Data analysts
- IT personnel preparing to engage with Oracle data warehousing solutions
- Business intelligence teams
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.