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Course Outline
Foundamentals of Oracle Data Warehousing
- Data warehouse architecture and typical use cases
- Distinguishing between OLTP and OLAP workloads
- Key elements of an Oracle DW solution
Designing Warehouse Schemas
- Dimensional modeling approaches, including star and snowflake schemas
- Structure of fact and dimension tables
- Managing slowly changing dimensions (SCD)
Data Loading and ETL Methodologies
- Designing ETL processes leveraging SQL and PL/SQL
- Utilizing external tables and SQL*Loader
- Implementing incremental loads and CDC (Change Data Capture)
Partitioning and Performance Enhancement
- Partitioning techniques: range, list, and hash
- Leveraging query pruning and parallel processing
- Best practices for partition-wise joins
Compression and Storage Efficiency
- Implementing hybrid columnar compression
- Strategies for data archival
- Balancing performance and cost through storage optimization
Sophisticated Query and Analytics Capabilities
- Use of materialized views and query rewrite mechanisms
- Application of analytical SQL functions (RANK, LAG, ROLLUP)
- Conducting time-based analysis and generating real-time reports
Monitoring and Optimizing the Data Warehouse
- Tracking and analyzing query performance
- Managing resource usage and workloads
- Effective indexing strategies for data warehousing
Recap and Future Directions
Requirements
- Familiarity with SQL and fundamental Oracle database concepts
- Prior experience utilizing Oracle 12c or 19c in administrative or development capacities
- Foundational understanding of data warehousing principles
Target Audience
- Data warehouse developers
- Database administrators
- Business intelligence specialists
21 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
