Data Warehousing and Business Intelligence Systems

Information Technology and Digital Systems October 25, 2025
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Introduction

This comprehensive course covers the design, implementation, and management of data warehousing and business intelligence systems. Participants will learn dimensional modeling, ETL processes, data integration techniques, and BI platform configuration. The program addresses both traditional data warehouse architectures and modern approaches like data lakes and cloud data platforms. Through practical exercises and real-world case studies, attendees will gain the skills to build robust data infrastructure that supports effective business decision-making and analytics capabilities.

Objectives

Key learning objectives for this course include:

  • Design and implement effective data warehouse architectures
  • Develop dimensional models using star and snowflake schemas
  • Build and optimize ETL/ELT processes for data integration
  • Implement data quality and governance frameworks
  • Configure and manage business intelligence platforms
  • Design dashboards and reports for business users
  • Manage data warehouse performance and scalability
  • Integrate traditional data warehouses with modern data platforms
  • Develop data warehouse security and access controls

Target Audience

  • Data Architects
  • BI Developers
  • Data Engineers
  • Database Administrators
  • Business Analysts
  • IT Managers
  • Data Scientists
  • ETL Developers

Methodology

  • Data modeling workshops
  • ETL development exercises
  • Case studies of BI implementations
  • Tool evaluation and selection exercises
  • Performance tuning simulations
  • Group discussions on architecture choices
  • Individual project work

Personal Impact

  • Enhanced data architecture and modeling skills
  • Improved ETL development and optimization abilities
  • Stronger business requirements analysis skills
  • Increased confidence in BI platform management
  • Better understanding of data governance principles
  • Professional growth in data management career

Organizational Impact

  • Improved data-driven decision making
  • Enhanced business intelligence capabilities
  • Better data quality and consistency
  • Reduced reporting development time
  • Increased user self-service capabilities
  • Stronger competitive intelligence

Course Outline

Data Warehousing Fundamentals

Architecture Concepts
  • Data warehouse concepts and benefits
  • Kimball vs. Inmon methodologies
  • Data warehouse architecture patterns
  • Cloud data warehouse options
Requirements Analysis
  • Business requirements gathering
  • Source system analysis
  • Data profiling and assessment
  • Stakeholder management

Dimensional Modeling

Model Design
  • Dimensional modeling principles
  • Star schema vs. snowflake schema
  • Fact table design and granularity
  • Dimension table design and hierarchies
Advanced Modeling
  • Slowly changing dimensions
  • Bridge tables and many-to-many relationships
  • Conformed dimensions and data bus architecture
  • Aggregate tables and summary design

ETL Development

ETL Processes
  • ETL vs. ELT approaches
  • Data extraction techniques
  • Data transformation and cleansing
  • Data loading strategies
ETL Tools
  • SSIS development and deployment
  • Informatica PowerCenter basics
  • Cloud data integration tools
  • Custom ETL scripting

Data Quality & Governance

Quality Management
  • Data quality dimensions and metrics
  • Data profiling and assessment
  • Data cleansing and standardization
  • Data quality monitoring
    • Governance Framework
    • Data governance principles
    • Metadata management
    • Data lineage and impact analysis
    • Master data management integration

    BI Platform Configuration

    Platform Selection
    • BI platform evaluation criteria
    • On-premise vs. cloud BI solutions
    • Self-service BI tools
    • Mobile BI considerations
    Implementation
    • BI security model implementation
    • Report scheduling and distribution
    • Performance optimization
    • User training and support

    Performance Tuning

    Optimization Techniques
    • Query performance optimization
    • Indexing strategies for data warehouses
    • Partitioning and compression
    • Materialized views and aggregates
    Monitoring & Maintenance
    • Performance monitoring tools
    • Capacity planning
    • Backup and recovery strategies
    • Data archive and purge policies

    Modern Data Architecture

    Evolution Trends
    • Data lakes and data mesh architectures
    • Real-time data integration
    • Cloud data platforms
    • Data virtualization
    Future Directions
    • AI and machine learning integration
    • DataOps methodologies
    • Data catalog implementation
    • Emerging technologies in BI

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