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Course Outline
Introduction to Oracle Data Warehousing
- Data warehouse architecture and practical use cases.
- Distinctions between OLTP and OLAP workloads.
- Key components of an Oracle Data Warehouse solution.
Warehouse Schema Design
- Dimensional modeling approaches: star and snowflake schemas.
- Structure and function of fact and dimension tables.
- Managing slowly changing dimensions (SCD).
Data Loading and ETL Strategies
- Designing ETL processes utilizing SQL and PL/SQL.
- Implementation of external tables and SQL*Loader.
- Techniques for incremental loads and Change Data Capture (CDC).
Partitioning and Performance
- Partitioning methodologies: range, list, and hash.
- Query pruning and parallel processing mechanisms.
- Partition-wise joins and recommended best practices.
Compression and Storage Optimization
- Hybrid columnar compression techniques.
- Strategies for data archival.
- Balancing storage optimization for performance and cost-efficiency.
Advanced Query and Analytics Features
- Materialized views and automatic query rewrite.
- Utilization of analytical SQL functions (RANK, LAG, ROLLUP).
- Time-based analysis and real-time reporting capabilities.
Monitoring and Tuning the Data Warehouse
- Monitoring and assessing query performance.
- Managing resource usage and workload distribution.
- Effective indexing strategies for warehousing environments.
Summary and Next Steps
Requirements
- A solid grasp of SQL and core Oracle database fundamentals.
- Prior experience with Oracle 12c/19c in either 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.