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Course Outline
Foundational Data Warehousing
- Defining the data warehouse concept.
- The value of warehousing in analytics and reporting.
- Warehousing capabilities provided by Oracle Database 19c.
Architecture of Oracle Data Warehouses
- Essential components: source data, ETL, staging areas, and presentation layers.
- Comparison of star and snowflake schema designs.
- Oracle utilities for managing data warehouse environments.
Data Modeling Principles
- The role of fact and dimension tables.
- Managing surrogate keys and data granularity.
- Understanding the basics of slowly changing dimensions (SCD).
Introduction to ETL Workflows
- Overview of ETL processes and supported Oracle tools.
- Distinctions between batch loading and real-time ingestion.
- Addressing challenges in data integration and quality assurance.
Querying and Reporting Fundamentals
- Differentiating OLAP and OLTP workloads.
- Oracle’s approach to optimizing data warehouse queries.
- Introduction to materialized views and data aggregation.
Strategy and Scalability for Oracle Warehouses
- Considerations for hardware and system architecture.
- Advantages of data partitioning and compression.
- Overview of Oracle licensing and available features.
Practical Applications and Best Practices
- Analysis of warehouse design case studies.
- Recommended practices for planning Oracle data warehouse projects.
- Initiating a pilot implementation phase.
Conclusion and Future Directions
Requirements
- Familiarity with relational database systems.
- Foundational knowledge of SQL.
- No prior exposure to Oracle data warehousing is necessary.
Target Audience
- Data analysts.
- IT professionals preparing to engage with Oracle data warehouse technologies.
- Business intelligence teams.
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.