Data Warehousing

Training course

Overview

Data Warehousing is a professional training course designed to provide participants with a comprehensive understanding of the principles, architectures, technologies, and practices used to build reliable enterprise data warehouse environments. The course explores how organizations collect, integrate, transform, store, and organize data from multiple operational and external sources to support business intelligence, analytics, reporting, and strategic decision-making. Participants will develop a strong foundation in data warehouse concepts, dimensional modeling, ETL and ELT processes, data integration, metadata management, data quality, and analytical data structures.

The course provides practical guidance on designing and implementing modern data warehouse solutions using established data management principles, dimensional modeling techniques, and industry best practices. Participants will examine enterprise data warehouse architectures, staging areas, data marts, fact and dimension tables, star and snowflake schemas, slowly changing dimensions, surrogate keys, aggregation strategies, partitioning, indexing, and workload optimization. Practical exercises and case studies will enable participants to translate business requirements into effective analytical data models and warehouse structures.

Participants will also explore the operational and governance considerations required to maintain secure, scalable, high-quality, and dependable data warehouse environments. Topics include ETL/ELT orchestration, data validation, data lineage, metadata, master and reference data, security, access controls, monitoring, testing, performance management, backup and recovery, and data warehouse lifecycle management. The course also introduces cloud data warehousing, modern data platforms, lakehouse concepts, automation, and integration with contemporary business intelligence and analytics ecosystems.

By the end of the training, participants will be able to evaluate business and analytical requirements, design appropriate data warehouse architectures, develop dimensional models, plan data integration workflows, optimize warehouse performance, and establish effective governance and operational controls. Through hands-on exercises, real-world scenarios, case studies, and a practical capstone project, participants will gain the skills required to contribute to data warehouse initiatives and support enterprise analytics, reporting, and data-driven transformation programs.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Data warehouse developers and engineers

• Database administrators and database developers

• Data analysts and business intelligence professionals

• Data engineers and analytics engineers

• Business intelligence developers

• Data architects and enterprise architects

• IT professionals responsible for data platforms and analytics

• Reporting and management information professionals

• Data quality and data governance professionals

• Application developers working with analytical data environments

• Project managers and technical leads involved in data warehouse initiatives

• Managers and supervisors responsible for reporting, analytics, and data management

• Consultants supporting business intelligence and data transformation projects

Course Objectives

By the end of the training, participants will be able to:

• Explain the purpose, principles, architecture, and business value of data warehousing

• Distinguish between operational databases, data warehouses, data marts, data lakes, and modern analytical platforms

• Gather and translate business intelligence and analytical requirements into data warehouse designs

• Design conceptual, logical, and physical data warehouse architectures

• Apply dimensional modeling principles using fact tables, dimension tables, star schemas, and snowflake schemas

• Design effective keys, relationships, hierarchies, measures, attributes, and analytical structures

• Develop appropriate ETL and ELT strategies for integrating data from heterogeneous sources

• Apply data quality, validation, cleansing, metadata, lineage, and governance practices

• Implement slowly changing dimensions, historical tracking, incremental loading, and data transformation techniques

• Improve warehouse performance using indexing, partitioning, aggregation, query optimization, and workload management

• Apply appropriate security, access control, backup, recovery, and business continuity practices

• Evaluate cloud and modern data warehouse architectures and technology options

• Establish monitoring, testing, documentation, deployment, and operational management processes

• Assess data warehouse risks, scalability requirements, and lifecycle considerations

• Build an integrated data warehouse solution through a practical real-world capstone exercise

Course Content

Day 1: Foundations of Data Warehousing and Enterprise Architecture

Module 1: Data Warehouse Fundamentals, Architecture, and Requirements

Topics

  1. Introduction to Data Warehousing and Analytical Data Management
  2. Business Drivers, Objectives, and Value of Data Warehousing
  3. Operational Databases vs Data Warehouses vs Data Marts
  4. Data Warehouse Characteristics, Components, and Lifecycle
  5. Enterprise Data Warehouse, Departmental Warehouse, and Federated Architectures
  6. Data Warehouse Architecture Layers and Reference Architecture Patterns
  7. Source Systems, Staging Areas, Integration Layers, and Presentation Layers
  8. Business Intelligence, Reporting, Analytics, and Data Warehouse Relationships
  9. Data Warehouse Requirements Gathering and Stakeholder Analysis
  10. Case Study and Exercise: Defining a Data Warehouse Strategy for an Enterprise Organization

Day 2: Dimensional Modeling and Data Warehouse Design

Module 2: Dimensional Modeling, Schema Design, and Historical Data

Topics

  1. Fundamentals of Dimensional Modeling
  2. Fact Tables, Dimension Tables, Measures, and Attributes
  3. Star Schema Design and Analytical Query Structures
  4. Snowflake Schema Design and Normalized Dimensions
  5. Grain Definition and Business Process Modeling
  6. Primary Keys, Surrogate Keys, Natural Keys, and Referential Integrity
  7. Dimension Hierarchies, Degenerate Dimensions, Role-Playing Dimensions, and Junk Dimensions
  8. Slowly Changing Dimensions and Historical Data Management
  9. Advanced Fact Table Patterns, Accumulating Snapshots, Periodic Snapshots, and Transaction Facts
  10. Practical Exercise: Designing a Dimensional Model for a Sales and Customer Analytics Warehouse

Day 3: ETL, ELT, Data Integration, and Data Quality

Module 3: Data Integration, Transformation, Quality, and Metadata Management

Topics

  1. Introduction to ETL and ELT Architecture and Processing Patterns
  2. Source Data Profiling, Extraction Strategies, and Data Ingestion
  3. Data Transformation, Standardization, Cleansing, and Enrichment
  4. Full Loads, Incremental Loads, Change Data Capture, and Delta Processing
  5. ETL/ELT Workflow Design, Scheduling, Dependencies, and Orchestration
  6. Data Validation, Reconciliation, Error Handling, and Exception Management
  7. Data Quality Dimensions, Rules, Controls, and Quality Monitoring
  8. Metadata Management, Data Lineage, Business Glossaries, and Technical Metadata
  9. Data Warehouse Integration Standards, Documentation, Testing, and Deployment Practices
  10. Case Study and Exercise: Building an End-to-End Data Integration Pipeline

Day 4: Performance, Security, Reliability, and Modern Data Warehousing

Module 4: Warehouse Optimization, Security, Cloud Platforms, and Operational Management

Topics

  1. Data Warehouse Performance Management and Query Optimization
  2. Indexing, Partitioning, Clustering, Compression, and Storage Optimization
  3. Aggregations, Materialized Views, Caching, and Workload Optimization
  4. Concurrency, Workload Management, Capacity Planning, and Scalability
  5. Data Warehouse Security Architecture and Access Control
  6. Encryption, Data Masking, Auditing, Privacy, and Sensitive Data Protection
  7. Backup, Recovery, High Availability, Disaster Recovery, and Business Continuity
  8. Cloud Data Warehousing, Elastic Architectures, and Consumption-Based Platforms
  9. Modern Data Warehouse, Data Lake, Lakehouse, and Hybrid Architecture Patterns
  10. Practical Scenario: Diagnosing Performance, Security, and Scalability Problems in a Data Warehouse

Day 5: Governance, Operations, Advanced Architecture, and Capstone

Module 5: Data Warehouse Governance, Lifecycle Management, and Enterprise Implementation

Topics

  1. Data Warehouse Governance Frameworks, Policies, Standards, and Responsibilities
  2. Data Ownership, Stewardship, Master Data, Reference Data, and Governance Controls
  3. Data Warehouse Monitoring, Observability, Operational Metrics, and Service Management
  4. Data Warehouse Testing, Quality Assurance, Regression Testing, and Release Management
  5. Schema Evolution, Change Management, Versioning, and Lifecycle Management
  6. Advanced Data Warehouse Architecture, Scalability, and Enterprise Integration
  7. Data Warehouse Modernization, Migration, Cloud Adoption, and Transformation Strategies
  8. Data Warehouse Cost Management, Capacity Planning, KPIs, and Performance Measurement
  9. Case Study: Designing an Enterprise Data Warehouse Roadmap and Implementation Strategy
  10. Capstone Exercise: Design, Document, and Present an End-to-End Data Warehouse Solution

 

Course Schedules:

Dates Fees Location Apply