Training on Data Warehousing Fundamentals
Master data warehousing fundamentals and build robust data warehouses. Learn to design, implement, and maintain efficient data warehouses to support data-driven decision making.
Next intake
20 Jul 2026 · Nakuru
Duration
5 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Advanced
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
This course will provide a solid foundation for anyone looking to understand and work with data warehousing concepts and technologies. Participants will explore the key components of data warehousing, including data modeling, ETL (Extract, Transform, Load) processes, and data warehouse architecture. The course is designed to equip participants with the knowledge and skills necessary to design, implement, and manage data warehouses, ensuring data quality, integrity, and accessibility for business intelligence and decision-making processes.
Course Duration
5 Days
Who Should Attend
- IT professionals looking to specialize in data warehousing.
- Database administrators and developers seeking to enhance their skills.
- Business analysts and data professionals involved in data management.
- Project managers overseeing data warehousing projects.
- Students and professionals aspiring to enter the field of data warehousing and business intelligence.
What you'll walk away with
By the end of this course, participants will be able to:
- Understand the fundamental concepts and benefits of data warehousing
- Learn about different data warehousing architectures and their components
- Master data modeling techniques for designing dimensional data warehouses
- Acquire knowledge of ETL processes and tools for data extraction, transformation, and loading
- Explore online analytical processing (OLAP) concepts and technologies
- Gain hands-on experience with data warehousing tools and software
- Develop the ability to evaluate and select appropriate data warehousing solutions
What we cover, module by module
Module 1: Introduction to Data Warehousing
- Definition and characteristics of data warehouses
- Differences between operational systems and data warehouses
- Benefits of data warehousing for organizations
- Data warehousing lifecycle and methodologies
- Case Study: Implementing a data warehouse to improve enterprise reporting and decision-making
- Practical: Identify operational vs analytical data and design a simple warehouse structure
Module 2: Data Warehouse Architecture
- Components of a data warehouse: data sources, ETL, data warehouse, metadata repository
- Data warehouse architectures: centralized, federated, data mart
- Data warehouse implementation approaches: top-down, bottom-up, hybrid
- Data warehouse performance and scalability
- Case Study: Designing a scalable data warehouse for a multi-branch organization
- Practical: Draw a data warehouse architecture for a sample business
Module 3: Data Modeling for Data Warehousing
- Dimensional modeling concepts: stars, snowflakes, and fact constellations
- Entity-relationship modeling for data warehousing
- Data warehouse design principles and best practices
- Data quality and cleansing in data warehousing
- Case Study: Building a star schema for a retail sales analytics system
- Practical: Design a dimensional model for a business scenario
Module 4: ETL Processes and Tools
- ETL process overview: extraction, transformation, loading
- Data extraction techniques: batch, incremental, real-time
- Data transformation functions: cleansing, aggregation, filtering, joining
- Data loading methods: bulk load, index creation, partitioning
- ETL tools and technologies
- Case Study: Developing an ETL pipeline for enterprise reporting
- Practical: Simulate an ETL workflow using sample datasets
Module 5: Online Analytical Processing (OLAP)
- OLAP concepts and dimensions
- Multidimensional data structures: cubes, hypercubes
- OLAP operations: slicing, dicing, drilling down, roll-up
- OLAP tools and technologies
- Performance optimization for OLAP
- Case Study: Using OLAP cubes for sales performance analysis
- Practical: Perform OLAP-style analysis on a dataset using pivot-based tools
Where the change lands
Organizational Impact
-
Strengthen strategic decision-making with a single, reliable source of business data.
-
Increase operational efficiency by streamlining reporting and reducing manual data work.
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Foster a data-driven culture to uncover trends, reduce costs, and boost profitability.
Personal Impact
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Gain in-demand skills for a modern IT or data career.
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Progress into roles like data architecture, data engineering, or senior management.
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Lead and champion scalable, secure data infrastructure initiatives with confidence.
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
NakuruNext | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Kigali | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Accra | 20 Jul 2026 | 24 Jul 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Johannesburg | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Dakar | 27 Jul 2026 | 31 Jul 2026 | In-Person | Book |
- NakuruNext
20 Jul → 24 Jul·In-Person
Book this intake - Kigali
20 Jul → 24 Jul·In-Person
Book this intake - Accra
20 Jul → 24 Jul·In-Person
Book this intake - Kisumu
27 Jul → 31 Jul·In-Person
Book this intake - Johannesburg
27 Jul → 31 Jul·In-Person
Book this intake - Dakar
27 Jul → 31 Jul·In-Person
Book this intake
Common questions.
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Course finder
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For corporate teams
Training 10+ professionals?
We deliver Training on Data Warehousing Fundamentals in-house at your offices, at a venue we arrange, or fully virtual. Customise the curriculum against your KPIs, and get a bespoke price for the cohort size you need.