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Course Outline

Introduction to Data Warehousing

  • Defining a data warehouse
  • Benefits of data warehousing in analytics and reporting
  • Oracle Database 19c support for warehousing functions

Oracle Data Warehouse Architecture

  • Key components: source data, ETL processes, staging areas, and presentation layers
  • Star schema versus snowflake schema structures
  • Oracle tools for managing data warehouse environments

Data Modeling Concepts

  • Fact and dimension tables
  • Surrogate keys and levels of granularity
  • Introduction to slowly changing dimensions (SCD)

Introduction to ETL Processes

  • Overview of ETL and Oracle-supported tools
  • Batch loading versus real-time data loading
  • Challenges related to data integration and quality

Query and Reporting Concepts

  • Fundamental differences between OLAP and OLTP workloads
  • How Oracle optimizes queries for data warehouse environments
  • Introduction to materialized views and data aggregates

Planning and Scaling Oracle Warehouses

  • Considerations for hardware and architecture
  • 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 prior experience with Oracle data warehousing is required

Audience

  • Data analysts
  • IT professionals planning to engage with Oracle data warehousing
  • Business intelligence teams
 14 Hours

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