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 Duration 14 hours

Course Outline

Overview of Data Warehousing

  • Definition and purpose of a data warehouse.
  • Advantages of warehousing in analytics and reporting.
  • How Oracle Database 19c supports warehousing needs.

Architecture of Oracle Data Warehouses

  • Core elements: source data, ETL, staging areas, and presentation layers.
  • Comparison between star and snowflake schema designs.
  • Oracle utilities for managing data warehouse environments.

Principles of Data Modeling

  • Structure and function of fact and dimension tables.
  • Concepts of surrogate keys and data granularity.
  • Introduction to slowly changing dimensions (SCD).

Fundamentals of ETL Processes

  • General overview of ETL and supported Oracle tools.
  • Distinction between batch and real-time data loading.
  • Common challenges in data integration and quality management.

Concepts in Querying and Reporting

  • Differences between OLAP and OLTP workloads.
  • Methods Oracle uses to optimize queries in data warehouse contexts.
  • Introduction to materialized views and data aggregation.

Strategy for Planning and Scaling Oracle Warehouses

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

Practical Applications and Recommended Practices

  • Analysis of warehouse design case studies.
  • Best practices for structuring Oracle DW projects.
  • Steps to initiate a pilot implementation.

Recap and Future Directions

Requirements

  • Familiarity with relational database systems.
  • Fundamental proficiency in SQL.
  • No previous experience with Oracle data warehousing is necessary.

Target Audience

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

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