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Duration 21 hours
Course Outline
Foundations of Oracle Data Warehousing
- Architectural patterns and practical applications for data warehouses
- Distinguishing between OLTP and OLAP workloads
- Key elements of an Oracle DW implementation
Designing Warehouse Schemas
- Dimensional modeling techniques: star and snowflake structures
- Roles of fact and dimension tables
- Managing slowly changing dimensions (SCD)
Data Ingestion and ETL Approaches
- Designing ETL workflows leveraging SQL and PL/SQL
- Utilizing external tables and SQL*Loader for data movement
- Implementing incremental loads and CDC (Change Data Capture)
Partitioning and Performance Enhancement
- Various partitioning strategies: range, list, and hash
- Optimizing through query pruning and parallel processing
- Executing partition-wise joins and adhering to best practices
Compression and Storage Efficiency
- Implementing hybrid columnar compression
- Planning data archival strategies
- Balancing storage optimization for both performance and cost efficiency
Advanced Querying and Analytics Capabilities
- Leveraging materialized views and automated query rewrite
- Applying analytical SQL functions such as RANK, LAG, and ROLLUP
- Conducting time-based analysis and enabling real-time reporting
Monitoring and Data Warehouse Tuning
- Tracking and assessing query performance
- Managing resource allocation and workload balance
- Developing effective indexing strategies for warehousing environments
Recap and Future Directions
Requirements
- Solid comprehension of SQL and fundamental Oracle database concepts
- Practical experience with Oracle 12c/19c in administrative or development capacities
- Foundational awareness of data warehousing principles
Target Audience
- Data warehouse developers
- Database administrators
- Business intelligence specialists
Testimonials (1)
good explanation on each points and provide assignment for practices.