Oracle Database 19c: Data Warehousing Techniques

• An understanding of SQL and Oracle database fundamentals
• Experience working with Oracle 12c/19c in an administrative or development role
• Basic knowledge of data warehousing concepts

Audience

• Data warehouse developers
• Database administrators
• Business intelligence professionals

Oracle Database 19c is a powerful platform for building high-performance data warehouses that support business intelligence and analytics workloads.

This instructor-led, live training (online or onsite) is aimed at intermediate-level Oracle professionals who wish to implement and optimize data warehousing solutions using Oracle Database 19c.

By the end of this training, participants will be able to:

• Design and implement data warehouse schemas (star, snowflake).
• Use Oracle partitioning and compression for large datasets.
• Optimize ETL processes and query performance for analytics workloads.
• Leverage Oracle features for real-time and historical reporting.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Oracle Data Warehouse features in sample scenarios.
• Guided exercises focused on modeling, ETL, and performance tuning.

Course Customization Options

• To request a customized training for this course based on your organization's data models or reporting needs, please contact us to arrange.

Introduction to Oracle Data Warehousing

• Data warehouse architecture and use cases
• OLTP vs OLAP workloads
• Core components of an Oracle DW solution

Warehouse Schema Design

• Dimensional modeling: star and snowflake schemas
• Fact and dimension tables
• Handling slowly changing dimensions (SCD)

Data Loading and ETL Strategies

• ETL process design using SQL and PL/SQL
• Using external tables and SQL*Loader
• Incremental loads and CDC (Change Data Capture)

Partitioning and Performance

• Partitioning methods: range, list, 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 performance and cost

Advanced Query and Analytics Features

• Materialized views and query rewrite
• Analytical SQL functions (RANK, LAG, ROLLUP)
• Time-based analysis and real-time reporting

Monitoring and Tuning the Data Warehouse

• Monitoring query performance
• Resource usage and workload management
• Indexing strategies for warehousing

Summary and Next Steps