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Course Outline
Introduction to Data Warehousing in Oracle
- Data warehouse architecture and practical use cases
- Distinguishing OLTP from OLAP workloads
- Essential components of an Oracle DW environment
Designing Warehouse Schemas
- Dimensional modeling approaches, including star and snowflake structures
- Implementation of fact and dimension tables
- Managing slowly changing dimensions (SCD)
Strategies for Data Loading and ETL
- Designing ETL workflows using SQL and PL/SQL
- Utilizing external tables and SQL*Loader for data ingestion
- Managing incremental loads and Change Data Capture (CDC)
Partitioning and Performance Enhancement
- Partitioning techniques: range, list, and hash
- Implementing query pruning and parallel processing
- Best practices for partition-wise joins
Compression and Storage Efficiency
- Applying hybrid columnar compression
- Developing data archival strategies
- Balancing storage optimization for performance and cost-effectiveness
Advanced Querying and Analytical Capabilities
- Utilizing materialized views and query rewriting
- Employing analytical SQL functions like RANK, LAG, and ROLLUP
- Conducting time-based analyses and real-time reporting
Data Warehouse Monitoring and Tuning
- Tracking and analyzing query performance
- Managing resource utilization and workload distribution
- Developing indexing strategies specific to warehousing
Conclusion and Future Steps
Requirements
- A solid grasp of SQL and fundamental Oracle database principles.
- Prior experience with Oracle 12c/19c in either an administrative or development capacity.
- Foundational understanding of data warehousing concepts.
Target Audience
- Data warehouse developers
- Database administrators
- Business intelligence specialists
21 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
