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Course Outline
Introduction to Oracle Data Warehousing
- Data warehouse architecture and application scenarios.
- Comparing OLTP and OLAP workloads.
- Key components of an Oracle DW solution.
Warehouse Schema Design
- Dimensional modeling: exploring star and snowflake schemas.
- Working with fact and dimension tables.
- Managing slowly changing dimensions (SCD).
Data Loading and ETL Strategies
- Designing ETL processes using SQL and PL/SQL.
- Leveraging external tables and SQL*Loader.
- Implementing incremental loads and Change Data Capture (CDC).
Partitioning and Performance Optimization
- Partitioning techniques: range, list, and 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 both performance and cost-efficiency.
Advanced Query and Analytics Features
- Materialized views and query rewrite capabilities.
- Analytical SQL functions such as RANK, LAG, and ROLLUP.
- Time-based analysis and real-time reporting.
Monitoring and Tuning the Data Warehouse
- Monitoring query performance.
- Resource usage and workload management.
- Indexing strategies specific to data warehousing.
Summary and Next Steps
Requirements
- Proficiency in SQL and foundational knowledge of Oracle databases.
- Practical experience administering or developing with Oracle 12c/19c.
- Fundamental understanding of data warehousing principles.
Target Audience
- Data warehouse developers.
- Database administrators.
- Business intelligence specialists.
21 Hours
Testimonials (1)
good explanation on each points and provide assignment for practices.
