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Course Outline
Introduction to Data Warehousing
- Defining what a data warehouse is.
- The benefits of warehousing in analytics and reporting.
- How Oracle Database 19c supports warehousing.
Oracle Data Warehouse Architecture
- Essential components: source data, ETL, staging, and presentation layers.
- Comparing star and snowflake schemas.
- Oracle tools for managing data warehouse environments.
Data Modeling Concepts
- Understanding fact and dimension tables.
- The role of surrogate keys and granularity.
- Basics of slowly changing dimensions (SCD).
Introduction to ETL Processes
- An overview of ETL and Oracle-supported tools.
- Differentiating between batch and real-time data loading.
- Challenges related to data integration and quality.
Query and Reporting Concepts
- Fundamentals of OLAP versus OLTP workloads.
- Mechanisms Oracle uses to optimize queries for data warehouses.
- An introduction to materialized views and aggregates.
Planning and Scaling Oracle Warehouses
- Considerations regarding hardware and architecture.
- The advantages 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.
- Steps to initiate a pilot implementation.
Summary and Next Steps
Requirements
- A foundational understanding of relational databases.
- Basic proficiency in SQL.
- No previous experience with Oracle data warehousing is necessary.
Target Audience
- Data analysts.
- IT personnel intending to work with Oracle data warehousing.
- Business intelligence teams.
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.