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Course Outline
Introduction to Oracle Data Warehousing
- Data warehouse architecture and applicable use cases.
- Distinguishing between OLTP and OLAP workloads.
- Essential components of an Oracle Data Warehouse solution.
Designing Warehouse Schemas
- Dimensional modeling, focusing on star and snowflake schemas.
- Understanding fact and dimension tables.
- Managing Slowly Changing Dimensions (SCD).
Data Loading and ETL Strategies
- Designing ETL processes utilizing SQL and PL/SQL.
- Leveraging external tables and SQL*Loader.
- Implementing incremental loads and Change Data Capture (CDC).
Partitioning and Performance
- Partitioning approaches: range, list, and hash.
- Query pruning and parallel processing techniques.
- Partition-wise joins and associated best practices.
Compression and Storage Optimization
- Hybrid columnar compression methods.
- Strategies for data archival.
- Optimizing storage configurations for performance and cost-efficiency.
Advanced Query and Analytics Features
- Materialized views and query rewriting capabilities.
- Analytical SQL functions, including RANK, LAG, and ROLLUP.
- Time-based analysis and real-time reporting mechanisms.
Monitoring and Tuning the Data Warehouse
- Monitoring and assessing query performance.
- Managing resource usage and workloads.
- Indexing strategies specific to data warehousing.
Conclusion and Future Directions
Requirements
- Solid understanding of SQL and core Oracle database principles.
- Practical experience with Oracle 12c/19c in an administrative or development capacity.
- Fundamental 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.
