Kimball Dimensional Modelling for Business Intelligence Training Course

Course overview

Business intelligence teams often inherit reporting environments built around source-system tables, inconsistent definitions and dashboards that cannot reconcile across finance, sales, operations and customer data. The result is slow report delivery, duplicated transformation logic and arguments over measures such as revenue, margin, active customer or order fulfilment. This course equips participants to use the Kimball dimensional modelling method to create business-facing data warehouse designs that are understandable, extensible and fit for consistent analytical reporting.

Participants learn to declare the grain of a business process, identify facts and dimensions, create star schemas, define conformed dimensions and manage enterprise data marts through the Kimball bus architecture. The course covers transaction, periodic snapshot and accumulating snapshot fact tables; additive, semi-additive and non-additive measures; surrogate keys; slowly changing dimensions; degenerate dimensions; role-playing dimensions; and dimensional designs for common processes. Participants also practise documenting source-to-target rules, data-quality controls and semantic definitions for implementation in modern BI platforms.

Instructor-led modelling sessions are supported by worked examples, design critiques and hands-on workshops using SQL Server, Power BI and dbt concepts. Across the two weeks, participants develop a dimensional model for a realistic multi-process retail and fulfilment case, progressing from stakeholder questions and source data to a bus matrix, star schemas, transformation rules and a reporting-ready semantic design. They leave with a reviewed modelling pack that can be adapted to a live warehouse, lakehouse or BI modernisation initiative.

The course is designed for intermediate data professionals who need to design, review or govern analytical data structures rather than simply build reports from existing tables. It is equally useful to technical leads and BI managers responsible for establishing shared modelling standards across delivery teams.

Course objectives

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

• Declare the grain and business process for a dimensional fact table before selecting facts and dimensions
• Construct star schemas using Kimball fact-table and dimension-table design rules
• Create a Kimball bus matrix that identifies conformed dimensions across enterprise business processes
• Design transaction, periodic snapshot and accumulating snapshot fact tables for distinct analytical needs
• Implement slowly changing dimension Type 1, Type 2 and Type 3 handling with surrogate keys
• Classify additive, semi-additive and non-additive measures and specify valid aggregation behaviour
• Document source-to-target mappings, business definitions and data-quality rules for a dimensional model
• Produce a reviewed dimensional modelling pack containing schemas, bus matrix, grain statements and implementation notes

Benefits of attending

Target competencies

Who should attend

• BI Developers — who need to build reliable reporting models rather than layer calculations over operational tables
• Data Warehouse Developers — who design fact and dimension tables for enterprise analytics workloads
• Data Engineers — who translate source-system data into curated warehouse or lakehouse structures
• Analytics Engineers — who need repeatable dimensional transformations and semantic-model-ready datasets
• Data Architects — who set modelling standards and coordinate cross-domain conformed dimensions
• BI and Data Managers — who must govern metric consistency and assess warehouse design decisions

Requirements and prerequisites

Participants should have practical experience working with relational data, including tables, primary and foreign keys, joins, basic SQL SELECT statements and common business measures such as sales, quantity and margin. Familiarity with a reporting or BI tool, such as Power BI, Tableau or Looker, is helpful because exercises connect models to analytical use cases. Participants should also be able to discuss a business process with data owners. Prior data warehouse design experience is not required, and no advanced SQL, Python, data science, cloud platform certification or prior Kimball training is assumed.

Training methodology

The instructor introduces each Kimball technique through a business question, a source-data scenario and a completed model before participants apply it themselves. Short design lectures alternate with whiteboard grain discussions, SQL-oriented table inspections and team modelling workshops. Participants critique competing schema options for sales, inventory and order fulfilment, including the consequences for Power BI reporting. Daily exercises add artefacts to a single case-study modelling pack. On day 10, each participant presents an implementation plan for applying the method to a selected business process in their organisation.

Course outline

Day 1: Kimball foundations and business-process framing

• Kimball dimensional modelling principles and analytical use cases
• Operational systems versus dimensional data warehouse structures
• Business process selection and stakeholder question discovery
• The four-step dimensional design process
• Fact-table grain statements and their design consequences
• Facts, dimensions and descriptive context
• Introduction to the enterprise bus architecture

Workshop: Analyse a retail reporting brief and produce a first-pass business-process inventory with grain statements for three candidate fact tables.

Day 2: Fact tables and measurement design

• Transaction fact tables for event-level analysis
• Periodic snapshot fact tables for performance trends
• Accumulating snapshot fact tables for lifecycle milestones
• Additive, semi-additive and non-additive measure classification
• Derived measures and ratio calculation rules
• Factless fact tables for coverage and event tracking
• Null handling and unknown measurement conventions

Workshop: Design sales, daily inventory and order-lifecycle fact tables, then classify each measure's permitted aggregation behaviour.

Day 3: Dimension design for analytical usability

• Dimension attributes, hierarchies and descriptive labels
• Surrogate keys and natural business keys
• Date dimensions, fiscal calendars and special dates
• Role-playing dimensions for multiple date relationships
• Degenerate dimensions and transaction identifiers
• Junk dimensions for low-cardinality indicators
• Outrigger and snowflake decisions in Kimball designs

Workshop: Create a customer, product and date-dimension specification with surrogate keys, hierarchies and reporting attributes.

Day 4: Slowly changing dimensions and history

• Business reasons for preserving dimensional history
• Slowly changing dimension Type 1 overwrite processing
• Slowly changing dimension Type 2 row-version processing
• Slowly changing dimension Type 3 alternate attribute processing
• Effective dates, current flags and version numbers
• Late-arriving dimensions and inferred member records
• Restatement and historical reporting implications

Workshop: Model customer territory and product-category changes using Type 1, Type 2 and Type 3 rules, producing a change-handling matrix.

Day 5: Conformed dimensions and enterprise integration

• Conformed dimensions and identical attribute meaning
• The Kimball bus matrix as an integration blueprint
• Enterprise data marts and incremental delivery
• Cross-process analysis using shared customer and product dimensions
• Master data alignment and source-system conflicts
• Dimension governance and ownership decisions
• Model scope boundaries and subject-area prioritisation

Workshop: Build a bus matrix for sales, returns, inventory and fulfilment processes and identify the conformance decisions required.

Day 6: Advanced dimensional patterns

• Many-to-many relationships and bridge tables
• Multivalued dimensions and group keys
• Recursive hierarchies and organisational structures
• Parent-child relationships in dimensional reporting
• Mini-dimensions for rapidly changing attributes
• Behavioural and demographic profile dimensions
• Handling heterogeneous product and service offerings

Workshop: Resolve a customer-to-account many-to-many reporting requirement by designing a bridge table and allocation approach.

Day 7: Source mapping and transformation design

• Source-to-target mapping document structure
• Business-key matching and surrogate-key lookup logic
• Change data capture and incremental loading patterns
• Late-arriving facts and dimension lookup exceptions
• Data-quality rules for dimensions and measures
• Audit columns, lineage and reconciliation controls
• dbt model layers for dimensional transformations

Workshop: Draft source-to-target mappings and transformation rules for a sales star schema, including key lookups and data-quality checks.

Day 8: Physical implementation and BI semantic use

• Star-schema physical design in SQL Server
• Fact-table loading order and dimension dependency management
• Indexing and partitioning considerations for large facts
• Power BI relationships and filter propagation
• Import, DirectQuery and composite model implications
• DAX measure placement and dimensional filtering
• Semantic model naming and business definition standards

Workshop: Map a completed star schema into a Power BI semantic model and define relationship, filter-direction and measure design choices.

Day 9: Design review, testing and governance

• Dimensional model review checklist
• Grain validation and duplicate-row testing
• Reconciliation of facts to operational source totals
• Dimension completeness and referential-integrity testing
• Aggregation testing for semi-additive measures
• Security, privacy and sensitive attribute considerations
• Model documentation and change-control practices

Workshop: Conduct a peer design review of a fulfilment model and produce a defect log, test plan and prioritised remediation actions.

Day 10: Capstone modelling and workplace application

• Capstone case requirements and stakeholder questions
• End-to-end four-step dimensional design application
• Bus matrix refinement and conformance decisions
• Fact and dimension schema presentation techniques
• Implementation sequencing and release planning
• Design risks, assumptions and decision logging
• Workplace adoption plan for Kimball standards

Workshop: Present a complete dimensional modelling pack for the case study, including bus matrix, star schemas, mappings, test controls and a 90-day application plan.

Tools & standards covered

Microsoft SQL Server, Microsoft Power BI, dbt, Kimball Enterprise Data Warehouse Bus Matrix

A typical training day

08:30 – 10:30	First session
10:30 – 10:45	Refreshment break
10:45 – 12:30	Second session
12:30 – 13:30	Lunch and networking
13:30 – 15:00	Third session
15:00 – 15:15	Refreshment break
15:15 – 16:30	Workshop and daily review

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions