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Course Outline

Introduction to Oracle Data Warehousing

  • Data warehouse architecture and use cases.
  • Comparison of OLTP and 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

Requirements

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

Audience

  • Data warehouse developers.
  • Database administrators.
  • Business intelligence professionals.
 21 Hours

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