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Duration 21 hours
Course Outline
Introduction to Oracle Data Warehousing
- Data warehouse architecture and applicable use cases.
- Differences between OLTP and OLAP workloads.
- Essential components of an Oracle DW solution.
Warehouse Schema Design
- Dimensional modeling techniques, including star and snowflake schemas.
- Structuring fact and dimension tables.
- Managing slowly changing dimensions (SCD).
Data Loading and ETL Strategies
- Designing ETL processes using SQL and PL/SQL.
- Utilizing external tables and SQL*Loader.
- Implementing incremental loads and CDC (Change Data Capture).
Partitioning and Performance
- Partitioning methods such as range, list, and hash.
- Leveraging query pruning and parallel processing.
- Best practices for partition-wise joins.
Compression and Storage Optimization
- Implementing hybrid columnar compression.
- Developing data archival strategies.
- Optimizing storage for both performance and cost efficiency.
Advanced Query and Analytics Features
- Utilizing materialized views and query rewrite capabilities.
- Applying analytical SQL functions like RANK, LAG, and ROLLUP.
- Conducting time-based analysis and real-time reporting.
Monitoring and Tuning the Data Warehouse
- Monitoring query performance metrics.
- Managing resource usage and workload distribution.
- Applying indexing strategies specific to warehousing environments.
Summary and Next Steps
Requirements
- Proficiency in SQL and fundamental Oracle database concepts.
- Experience with Oracle 12c/19c in either an administrative or development capacity.
- Fundamental understanding of data warehousing principles.
Target Audience
- Data warehouse developers.
- Database administrators.
- Business intelligence professionals.
Testimonials (1)
good explanation on each points and provide assignment for practices.
