Introduction To Data Warehousing

Course Info

Length: 1 Week

City: Kuala Lumpur

Type: In Classroom

Available Dates

  • May-20-2024

    Kuala Lumpur

  • June-17-2024

    Kuala Lumpur

  • July-15-2024

    Kuala Lumpur

  • Aug-19-2024

    Kuala Lumpur

  • Sep-16-2024

    Kuala Lumpur

  • Oct-21-2024

    Kuala Lumpur

  • Nov-18-2024

    Kuala Lumpur

  • Dec-16-2024

    Kuala Lumpur

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  • Apr-22-2024

    Barcelona

  • Apr-22-2024

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  • Apr-29-2024

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  • Apr-29-2024

    Paris

  • May-06-2024

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  • May-13-2024

    London

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    Istanbul

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  • June-03-2024

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  • June-10-2024

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  • July-15-2024

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  • July-29-2024

    Paris

  • July-29-2024

    Dubai

  • Aug-05-2024

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  • Aug-26-2024

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  • Aug-26-2024

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  • Sep-02-2024

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  • Sep-09-2024

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  • Sep-09-2024

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  • Sep-16-2024

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  • Sep-23-2024

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  • Sep-23-2024

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  • Sep-30-2024

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  • Sep-30-2024

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  • Oct-07-2024

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  • Oct-07-2024

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  • Oct-14-2024

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  • Oct-21-2024

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  • Nov-04-2024

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  • Nov-04-2024

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  • Nov-11-2024

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  • Nov-18-2024

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  • Nov-25-2024

    Singapore

  • Nov-25-2024

    Paris

  • Dec-02-2024

    Barcelona

  • Dec-02-2024

    Dubai

  • Dec-09-2024

    London

  • Dec-09-2024

    Amsterdam

  • Dec-16-2024

    Istanbul

  • Dec-23-2024

    Barcelona

  • Dec-23-2024

    Singapore

  • Dec-30-2024

    Paris

  • Dec-30-2024

    Dubai

Course Details

Course Outline

5 days course

Introduction to Data Warehouse

  • Data Warehousing: Concept, purpose, and evolution.
  • Features: Characteristics like subject-oriented, integrated, time-variant, non-volatile, etc.
  • Enterprise, Operational, Data Mart, etc.
  • Extraction, Transformation, Loading (ETL), Data Storage, Metadata, etc.
  • Decision support, analytics, reporting, etc.
  • Advantages: Improved data quality, decision-making, business insights, etc.
  • Disadvantages: Costly, complex implementation, potential data quality issues, etc.
  • ETL tools, reporting tools, data modeling tools, etc.
  • Business intelligence, CRM, ERP, etc.

Data Warehouse Modelling

  • Techniques and challenges in merging data from different sources.
  • Data about data, its types, usage and Repository: Centralized storage of metadata.
  • Data Cube: Multidimensional view of data for analysis.
  • Data Mart: Subset of a data warehouse focused on specific business areas.
  • Virtual Warehouse: Logical view of data from various sources.
  • Dimensions: Qualitative descriptive data for organizing and filtering facts.
  • ER Diagram: Entity-Relationship diagrams for data modeling.
  • Delivery Method: Strategies for implementation.
  • IT Strategy: Aligning technology with business goals and Training and testing for user adoption.
  • Technical Blueprint: Detailed technical plan for implementation.

System Processes

  • Process Flow: Workflow within a data warehouse.
  • Extract and Load Process: Getting data into the warehouse.
  • Clean and Transform Process: Preparing data for storage and analysis.
  • Backup and Archive: Data protection and long-term storage strategies.
  • Data Warehouse Architecture
  • Three-Tier Data Warehouse Architecture: Presentation, Application, Data layers.
  • Warehouse Models: Operational data store, enterprise data warehouse, etc.
  • Load, Warehouse, and Query Manager: Components managing data movement and access.

Data Warehouse OLAP, Relational and Multidimensional OLAP

  • Types of OLAP Servers: MOLAP, ROLAP, HOLAP, etc.
  • OLAP Operations: Slice, dice, drill-down, roll-up, pivot, etc.
  • OLAP Vs OLTP: Contrasting online analytical processing with transaction processing.
  • Relational OLAP: Using relational databases for OLAP.
  • Multidimensional OLAP: Cube-based data representation.
  • Three-Tier Data Warehouse Architecture: Reiterated in context with OLAP.
  • Data Warehouse Schemas
  • Star Schema, Snowflake Schema, Fact Constellation Schema: Different schema structures for organizing data.
  • Schema Definition: Defining relationships and structure in a data warehouse.

Horizontal and Vertical Partitioning

  • Introduction to Partitioning: Dividing data for optimization.
  • Horizontal Partitioning: Splitting by rows.
  • Vertical Partitioning: Splitting by columns.
  • Metadata Categories: Descriptive, structural, administrative, etc.
  • Role of Metadata: Importance and usage in a data warehouse.
  • System Managers: Overseeing overall system operations.
  • Process Managers: Handling specific processes within the system.
  • Security Requirements: Ensuring data privacy and integrity.
  • User Access: Controlling access levels.
  • Impact of Security on Design: How security considerations influence system design.
  • Hardware and Software Backup: Strategies for data protection.
  • Optimizing performance and Verifying functionality and performance.

Course Video