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Duration 14 hours
Course Outline
Fundamentals of Data Warehousing
- Defining the data warehouse concept.
- Advantages of warehousing for analytics and reporting.
- How Oracle Database 19c supports warehousing requirements.
Oracle Data Warehouse Architecture
- Core components: source data, ETL processes, staging areas, and presentation layers.
- Comparison of star and snowflake schema designs.
- Oracle tools utilized for managing data warehouse environments.
Data Modeling Principles
- Roles of fact and dimension tables.
- Understanding surrogate keys and data granularity.
- Introduction to Slowly Changing Dimensions (SCD).
Overview of ETL Processes
- ETL workflow overview and Oracle-supported tooling.
- Distinctions between batch and real-time data loading.
- Addressing challenges in data integration and quality assurance.
Querying and Reporting Strategies
- Differentiating between OLAP and OLTP workloads.
- How Oracle optimizes query performance for data warehouses.
- Introduction to materialized views and aggregate management.
Planning and Scaling Oracle Warehouses
- Considerations regarding hardware and architectural design.
- Benefits of data partitioning and compression.
- Overview of Oracle licensing and feature sets.
Real-World Applications and Best Practices
- Analysis of warehouse design case studies.
- Recommended practices for planning Oracle DW projects.
- Steps for initiating a pilot implementation.
Summary 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 preparing to engage with Oracle data warehousing environments.
- Business intelligence teams.
Testimonials (1)
good explanation on each points and provide assignment for practices.