Introduction To Data Warehousing

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Nov-11-2024

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Dec-02-2024

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Oct-13-2025

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Oct-20-2025

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Oct-27-2025

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Oct-27-2025

Istanbul
Nov-03-2025

Singapore
Nov-03-2025

Paris
Nov-10-2025

London
Nov-10-2025

Amsterdam
Nov-17-2025

Dubai
Nov-17-2025

Barcelona
Nov-24-2025

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Nov-24-2025

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Dec-01-2025

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Dec-01-2025

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Dec-08-2025

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Kuala Lumpur

Course Details

In the realm of data management, the foundational understanding of Data Warehousing is crucial. Over the span of this comprehensive course, we embark on a journey that unfolds the intricacies of Data Warehousing, beginning with an extensive exploration on Day 1. Here, we delve into the bedrock principles, examining the concepts, purpose, and evolution of Data Warehousing. We dissect its defining features—subject-oriented, integrated, time-variant, non-volatile—while navigating through various facets like Enterprise, Operational, Data Mart, and more.

The course navigates through the intricate processes of Extraction, Transformation, and Loading (ETL), embracing the significance of Data Storage and Metadata. Decision support, analytics, and reporting functionalities within this framework are dissected for comprehensive understanding.

Not merely a discourse on advantages, the course candidly addresses the potential downsides—highlighting its costs, complex implementations, and looming data quality issues. Moreover, it unpacks the arsenal of tools from ETL to reporting and data modeling, and their indispensable roles in the Data Warehouse ecosystem.

From delving into Data Warehouse modeling techniques and challenges to deciphering the intricacies of system processes and security considerations, this course aims to equip participants with a holistic comprehension of Data Warehousing's vast landscape. Each day, a new layer unfolds, enriching participants with a mosaic of knowledge that culminates in an adept understanding of not just the theoretical aspects but also the practical implementation and optimization of Data Warehousing systems.

By the end of this course, participants should be able to:

Comprehensive Understanding: Foster a profound comprehension of the foundational concepts, evolution, and purpose of Data Warehousing, elucidating its features and classifications.
Technical Proficiency: Equip participants with technical skills in Extraction, Transformation, and Loading (ETL) processes, along with adeptness in utilizing various Data Warehousing tools and technologies.
Strategic Application: Enable participants to discern the strategic significance of Data Warehousing in decision support, analytics, reporting, and its pivotal role in fostering improved data quality and informed decision-making.
Modeling and Architecture: Deepen understanding in Data Warehouse modeling techniques, system processes, and the architectural framework, including the multi-tier structure and warehouse models.
OLAP Mastery: Attain proficiency in Online Analytical Processing (OLAP), distinguishing between types of OLAP servers, operations, and the implementation of different Data Warehouse schemas.
Partitioning and Optimization: Grasp the principles of data partitioning—both horizontal and vertical—to optimize performance, alongside understanding metadata's role and the necessity of robust security measures.
Implementation and Maintenance: Prepare participants to outline IT strategies aligned with business goals, develop technical blueprints, oversee system processes, manage user access, and implement robust backup strategies to ensure data integrity and functionality.

Data Analysts and Scientists
Database Administrators
Business Intelligence Professionals
IT Managers and System Architects
Data Engineers and Developers
Managers and Executives
Graduate Students and Aspiring Data Professionals

Course Outline

5 days course

Day 1

Day 2

Day 3

Day 4

Day 5

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.

Introduction To Data Warehousing

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