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Course Outline
Introduction to Oracle Data Warehousing
- Data warehouse architectures and practical applications
- Distinctions between OLTP and OLAP workloads
- Essential elements of an Oracle DW solution
Designing Warehouse Schemas
- Dimensional modeling techniques, including star and snowflake schemas
- Structure of fact and dimension tables
- Managing slowly changing dimensions (SCD)
Data Ingestion and ETL Strategies
- Designing ETL workflows using SQL and PL/SQL
- Leveraging external tables and SQL*Loader
- Implementing incremental loads and Change Data Capture (CDC)
Partitioning and Performance Management
- Partitioning approaches: range, list, and hash
- Query pruning and parallel processing techniques
- Partition-wise joins and industry best practices
Compression and Storage Optimization
- Hybrid columnar compression methods
- Strategies for data archival
- Balancing storage optimization for performance and cost efficiency
Advanced Query and Analytics Capabilities
- Utilizing materialized views and query rewriting
- Applying analytical SQL functions such as RANK, LAG, and ROLLUP
- Conducting time-based analyses and real-time reporting
Monitoring and Tuning the Data Warehouse
- Tracking and analyzing query performance
- Managing resource usage and workload distribution
- Developing indexing strategies specific to warehousing
Summary and Future Directions
Requirements
- Familiarity with SQL and foundational Oracle database principles
- Practical experience with Oracle 12c/19c in an administrative or development capacity
- Foundational knowledge of data warehousing methodologies
Target Audience
- Data warehouse developers
- Database administrators
- Business intelligence analysts
21 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
