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Course Outline
Foundations of Data Warehousing
- Defining the role of a data warehouse
- Advantages of warehousing for analytics and reporting
- Warehousing capabilities provided by Oracle Database 19c
Oracle Data Warehouse Structure
- Essential elements: source data, ETL pipelines, staging areas, and presentation layers
- Comparison of star and snowflake schema designs
- Oracle utilities for managing DW environments
Data Modeling Principles
- Fact and dimension table structures
- The role of surrogate keys and data granularity
- Introduction to Slowly Changing Dimensions (SCD)
Fundamentals of ETL Workflows
- Overview of ETL processes and compatible Oracle tools
- Distinctions between batch processing and real-time data loading
- Addressing data integration and quality challenges
Querying and Reporting Strategies
- Core differences between OLAP and OLTP workloads
- Oracle mechanisms for optimizing warehouse queries
- Conceptual introduction to materialized views and aggregate structures
Strategy for Scaling Oracle Warehouses
- Hardware and architectural considerations for growth
- The impact of partitioning and compression on performance
- Overview of Oracle licensing and feature sets
Practical Applications and Industry Standards
- Review of warehouse design case studies
- Best practices for initiating Oracle DW projects
- Steps for launching a pilot implementation
Recap and Future Directions
Requirements
- A foundational comprehension of relational database systems
- Fundamental proficiency in SQL
- Prior experience with Oracle data warehousing is not a prerequisite
Intended Audience
- Data analysts
- IT personnel intending to engage with Oracle data warehousing solutions
- Business intelligence teams
14 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
