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Course Outline

Foundations of Data Warehousing

  • Defining the data warehouse
  • Advantages of warehousing for analytics and reporting tasks
  • Warehousing capabilities supported by Oracle Database 19c

Oracle Data Warehouse Structure

  • Core elements: source data, ETL, staging, and presentation layers
  • Comparative analysis of star and snowflake schemas
  • Oracle utilities for overseeing DW environments

Data Modeling Principles

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

Overview of ETL Workflows

  • Summary of ETL processes and compatible Oracle tools
  • Batch processing versus real-time data loading
  • Common hurdles in data integration and quality management

Querying and Reporting Essentials

  • Distinguishing between OLAP and OLTP workloads
  • Oracle’s approach to query optimization for warehouses
  • Introductory look at materialized views and aggregates

Strategy for Scaling Oracle Warehouses

  • Hardware and architectural planning considerations
  • Advantages of partitioning and compression techniques
  • Overview of Oracle licensing and feature sets

Practical Applications and Recommended Practices

  • Analysis of warehouse design case studies
  • Best practices for orchestrating Oracle DW projects
  • Initial steps for a pilot implementation

Recap and Future Directions

Requirements

  • Familiarity with relational database systems
  • Fundamental understanding of SQL
  • No previous experience with Oracle data warehousing is necessary

Intended Audience

  • Data analysts
  • IT personnel intending to engage with Oracle warehousing solutions
  • Business intelligence teams
 14 Hours

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