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Course Outline

Introduction to Data Warehousing

  • Definition and purpose of a data warehouse.
  • Advantages of warehousing in analytics and reporting.
  • Oracle Database 19c capabilities for warehousing.

Oracle Data Warehouse Architecture

  • Key components: source data, ETL, staging areas, and presentation layers.
  • Comparison of star and snowflake schemas.
  • Oracle tools for managing data warehouse environments.

Data Modeling Concepts

  • Understanding fact and dimension tables.
  • Surrogate keys and data granularity.
  • Introduction to slowly changing dimensions (SCD).

Introduction to ETL Processes

  • Overview of ETL and Oracle-supported tools.
  • Differences between batch and real-time data loading.
  • Challenges associated with data integration and quality assurance.

Query and Reporting Concepts

  • Fundamental differences between OLAP and OLTP workloads.
  • How Oracle optimizes queries specifically for data warehouses.
  • Introduction to materialized views and aggregate tables.

Planning and Scaling Oracle Warehouses

  • Considerations for hardware and architectural design.
  • Benefits of partitioning and data compression.
  • Overview of Oracle licensing and available features.

Use Cases and Best Practices

  • Case studies on warehouse design.
  • Best practices for planning Oracle data warehouse projects.
  • Steps for initiating a pilot implementation.

Summary and Next Steps

Requirements

  • A solid understanding of relational database principles.
  • Basic proficiency in SQL.
  • Prior experience with Oracle data warehousing is not required.

Target Audience

  • Data analysts.
  • IT professionals preparing to work with Oracle data warehousing solutions.
  • Business intelligence teams.
 14 Hours

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