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Course Outline
Introduction to Data Warehousing
- Defining a data warehouse.
- Advantages of warehousing in analytics and reporting.
- Oracle Database 19c support for warehousing.
Oracle Data Warehouse Architecture
- Key components: source data, ETL, staging, and presentation layers.
- Star versus snowflake schemas.
- Oracle tools for managing data warehouse environments.
Data Modeling Concepts
- Fact and dimension tables.
- Surrogate keys and granularity.
- Basics of slowly changing dimensions (SCD).
Introduction to ETL Processes
- Overview of ETL and Oracle-supported tools.
- Batch versus real-time data loading.
- Challenges related to data integration and quality.
Query and Reporting Concepts
- Fundamentals of OLAP versus OLTP workloads.
- How Oracle optimizes queries for data warehouses.
- Introduction to materialized views and aggregates.
Planning and Scaling Oracle Warehouses
- Considerations for hardware and architecture.
- Benefits of partitioning and compression.
- Overview of Oracle licensing and features.
Use Cases and Best Practices
- Case studies on warehouse design.
- Best practices for planning Oracle data warehouse projects.
- Getting started with a pilot implementation.
Summary and Next Steps
Requirements
- Understanding of relational databases.
- Basic proficiency in SQL.
- No prior experience with Oracle data warehousing is required.
Target Audience
- Data analysts.
- IT personnel planning to work with Oracle data warehousing.
- Business intelligence teams.
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.