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Course Outline
Introduction to Oracle Data Warehousing
- Data warehouse architecture and practical use cases
- Distinguishing between OLTP and OLAP workloads
- Key components of an Oracle DW solution
Warehouse Schema Design
- Dimensional modeling: exploring star and snowflake schemas
- Understanding fact and dimension tables
- Managing slowly changing dimensions (SCD)
Data Loading and ETL Strategies
- Designing ETL processes using SQL and PL/SQL
- Utilizing external tables and SQL*Loader
- Implementing incremental loads and CDC (Change Data Capture)
Partitioning and Performance
- Partitioning methods: range, list, and hash
- Query pruning and parallel processing techniques
- Partition-wise joins and industry best practices
Compression and Storage Optimization
- Hybrid columnar compression
- Strategies for data archival
- Optimizing storage for improved performance and cost-efficiency
Advanced Query and Analytics Features
- Materialized views and query rewrite capabilities
- Analytical SQL functions (RANK, LAG, ROLLUP)
- Time-based analysis and real-time reporting
Monitoring and Tuning the Data Warehouse
- Monitoring query performance
- Managing resource usage and workload
- Indexing strategies specific to warehousing
Summary and Next Steps
Requirements
- Proficiency in SQL and a solid grasp of Oracle database fundamentals
- Practical experience working with Oracle 12c/19c in administrative or development capacities
- Foundational knowledge of data warehousing principles
Target Audience
- Data warehouse developers
- Database administrators
- Business intelligence professionals
21 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.