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Course Outline

Introduction to Data Warehousing

  • Defining a data warehouse.
  • The value of warehousing in analytics and reporting.
  • Oracle Database 19c’s capabilities for warehousing.

Oracle Data Warehouse Architecture

  • Core components: source data, ETL, staging, and presentation layers.
  • Comparing star and snowflake schemas.
  • Oracle tools for managing data warehouse environments.

Data Modeling Fundamentals

  • Fact and dimension tables.
  • Surrogate keys and data granularity.
  • Basics of slowly changing dimensions (SCD).

Introduction to ETL Processes

  • Overview of ETL and Oracle-supported tools.
  • Batch loading versus real-time loading.
  • Challenges in data integration and quality assurance.

Query and Reporting Concepts

  • Basics of OLAP versus OLTP workloads.
  • How Oracle optimizes queries for data warehouses.
  • Introduction to materialized views and aggregates.

Planning and Scaling Oracle Warehouses

  • Hardware and architectural considerations.
  • Benefits of partitioning and compression.
  • Overview of Oracle licensing and features.

Use Cases and Best Practices

  • Case studies in warehouse design.
  • Best practices for planning Oracle data warehouse projects.
  • Launching a pilot implementation.

Summary and Next Steps

Requirements

  • A foundational understanding of relational databases.
  • Basic proficiency in SQL.
  • No previous experience with Oracle data warehousing is necessary.

Target Audience

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

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