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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
Testimonials (1)
good explanation on each points and provide assignment for practices.