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 Duration 21 hours

Course Outline

Fundamentals of Oracle Data Warehousing

  • Architecture of data warehouses and applicable use cases
  • Distinguishing between OLTP and OLAP workloads
  • Essential elements of an Oracle Data Warehouse solution

Architecting Warehouse Schemas

  • Dimensional modeling techniques: star and snowflake structures
  • Designing fact and dimension tables
  • Managing slowly changing dimensions (SCD)

Data Ingestion and ETL Methodologies

  • Designing ETL workflows utilizing SQL and PL/SQL
  • Employing external tables and SQL*Loader for data transfer
  • Implementing incremental loads and Change Data Capture (CDC)

Partitioning Strategies and Performance Enhancement

  • Partitioning techniques: range-based, list-based, and hash-based
  • Optimizing via query pruning and parallel processing
  • Best practices for partition-wise joins

Data Compression and Storage Efficiency

  • Applying hybrid columnar compression
  • Formulating data archival strategies
  • Balancing storage optimization for performance and cost-effectiveness

Sophisticated Query and Analytics Capabilities

  • Utilizing materialized views and automated query rewrite
  • Using analytical SQL functions such as RANK, LAG, and ROLLUP
  • Conducting time-series analysis and generating real-time reports

Data Warehouse Monitoring and Optimization

  • Tracking and analyzing query performance
  • Managing resource allocation and workload distribution
  • Developing indexing strategies specific to warehousing environments

Conclusion and Future Directions

Requirements

  • Proficiency in SQL and foundational Oracle database principles
  • Practical experience with Oracle 12c or 19c in administrative or development capacities
  • Fundamental understanding of data warehousing concepts

Target Audience

  • Data warehouse developers
  • Database administrators
  • Business intelligence specialists

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