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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

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