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Course Outline

Introduction to Data Warehousing

  • Defining what a data warehouse is.
  • The benefits of warehousing in analytics and reporting.
  • How Oracle Database 19c supports warehousing.

Oracle Data Warehouse Architecture

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

Data Modeling Concepts

  • Understanding fact and dimension tables.
  • The role of surrogate keys and granularity.
  • Basics of slowly changing dimensions (SCD).

Introduction to ETL Processes

  • An overview of ETL and Oracle-supported tools.
  • Differentiating between batch and real-time data loading.
  • Challenges related to data integration and quality.

Query and Reporting Concepts

  • Fundamentals of OLAP versus OLTP workloads.
  • Mechanisms Oracle uses to optimize queries for data warehouses.
  • An introduction to materialized views and aggregates.

Planning and Scaling Oracle Warehouses

  • Considerations regarding hardware and architecture.
  • The advantages 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.
  • Steps to initiate 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 intending to work with Oracle data warehousing.
  • Business intelligence teams.
 14 Hours

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