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Course Outline

Fundamentals of Data Warehousing

  • Defining the concept of a data warehouse.
  • The value of warehousing for analytics and reporting purposes.
  • How Oracle Database 19c supports warehousing operations.

Oracle Data Warehouse Structure

  • Core elements: source data, ETL processes, staging areas, and presentation layers.
  • Comparing star and snowflake schema models.
  • Oracle tools utilized for managing data warehouse environments.

Data Modeling Principles

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

Overview of ETL Processes

  • An introduction to ETL and the tools supported by Oracle.
  • Distinctions between batch loading and real-time ingestion.
  • Common challenges regarding data integration and quality assurance.

Querying and Reporting Fundamentals

  • Differentiating OLAP and OLTP workloads.
  • Mechanisms by which Oracle optimizes queries for data warehousing.
  • Getting acquainted with materialized views and aggregate functions.

Strategy and Scaling for Oracle Warehouses

  • Considerations regarding hardware and system architecture.
  • The benefits of data partitioning and compression.
  • An overview of Oracle licensing models and available features.

Practical Applications and Best Practices

  • Reviewing warehouse design case studies.
  • Recommended best practices for planning Oracle data warehouse projects.
  • Steps for initiating a pilot implementation.

Recap and Future Pathways

Requirements

  • Familiarity with relational database concepts.
  • Foundational knowledge of SQL syntax.
  • No previous hands-on experience with Oracle data warehousing is necessary.

Target Audience

  • Data analysts looking to expand their skill set.
  • IT personnel intending to engage with Oracle data warehousing projects.
  • Members of business intelligence teams.
 14 Hours

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