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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

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