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Course Outline
Introduction to Oracle Data Warehousing
- Data warehouse architecture and use cases.
- OLTP vs OLAP workloads.
- Core components of an Oracle DW solution.
Warehouse Schema Design
- Dimensional modeling: star and snowflake schemas.
- Fact and dimension tables.
- Handling slowly changing dimensions (SCD).
Data Loading and ETL Strategies
- ETL process design using SQL and PL/SQL.
- Using external tables and SQL*Loader.
- Incremental loads and CDC (Change Data Capture).
Partitioning and Performance
- Partitioning methods: range, list, hash.
- Query pruning and parallel processing.
- Partition-wise joins and best practices.
Compression and Storage Optimization
- Hybrid columnar compression.
- Data archival strategies.
- Optimizing storage for performance and cost.
Advanced Query and Analytics Features
- Materialized views and query rewrite.
- Analytical SQL functions (RANK, LAG, ROLLUP).
- Time-based analysis and real-time reporting.
Monitoring and Tuning the Data Warehouse
- Monitoring query performance.
- Resource usage and workload management.
- Indexing strategies for warehousing.
Summary and Next Steps
Requirements
- A solid understanding of SQL and Oracle database fundamentals.
- Practical experience working with Oracle 12c/19c in an administrative or development capacity.
- Foundational knowledge of data warehousing concepts.
Target Audience
- Data warehouse developers.
- Database administrators.
- Business intelligence professionals.
21 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
