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Course Outline
Introduction to Oracle Data Warehousing
- Overview of data warehouse architecture and typical use cases.
- Distinguishing between OLTP and OLAP workloads.
- Key components of an Oracle Data Warehouse solution.
Warehouse Schema Design
- Dimensional modeling techniques, focusing on star and snowflake schemas.
- Structure and function of fact and dimension tables.
- Strategies for managing slowly changing dimensions (SCD).
Data Loading and ETL Strategies
- Designing ETL processes leveraging SQL and PL/SQL.
- Utilizing external tables and SQL*Loader for data ingestion.
- Implementing incremental loads and Change Data Capture (CDC).
Partitioning and Performance Optimization
- Exploring partitioning methods such as range, list, and hash.
- Leveraging query pruning and parallel processing for speed.
- Best practices for partition-wise joins.
Compression and Storage Optimization
- Implementing hybrid columnar compression.
- Developing effective data archival strategies.
- Balancing storage optimization for both performance and cost efficiency.
Advanced Query and Analytics Features
- Using 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
- Techniques for monitoring and enhancing query performance.
- Managing resource usage and workload distribution.
- Developing indexing strategies specific to warehousing environments.
Summary and Next Steps
Requirements
- A solid grasp of SQL and fundamental Oracle database principles.
- Prior experience utilizing Oracle 12c/19c within administrative or development roles.
- A 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.