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 Duration 14 hours

Course Outline

Fundamentals of Data Warehousing

  • Defining the data warehouse concept.
  • Advantages of warehousing for analytics and reporting.
  • How Oracle Database 19c supports warehousing requirements.

Oracle Data Warehouse Architecture

  • Core components: source data, ETL processes, staging areas, and presentation layers.
  • Comparison of star and snowflake schema designs.
  • Oracle tools utilized for managing data warehouse environments.

Data Modeling Principles

  • Roles of fact and dimension tables.
  • Understanding surrogate keys and data granularity.
  • Introduction to Slowly Changing Dimensions (SCD).

Overview of ETL Processes

  • ETL workflow overview and Oracle-supported tooling.
  • Distinctions between batch and real-time data loading.
  • Addressing challenges in data integration and quality assurance.

Querying and Reporting Strategies

  • Differentiating between OLAP and OLTP workloads.
  • How Oracle optimizes query performance for data warehouses.
  • Introduction to materialized views and aggregate management.

Planning and Scaling Oracle Warehouses

  • Considerations regarding hardware and architectural design.
  • Benefits of data partitioning and compression.
  • Overview of Oracle licensing and feature sets.

Real-World Applications and Best Practices

  • Analysis of warehouse design case studies.
  • Recommended practices for planning Oracle DW projects.
  • Steps for initiating a pilot implementation.

Summary and Future Directions

Requirements

  • Familiarity with relational database systems.
  • Foundational knowledge of SQL.
  • No previous experience with Oracle data warehousing is necessary.

Target Audience

  • Data analysts.
  • IT personnel preparing to engage with Oracle data warehousing environments.
  • Business intelligence teams.

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