Data Vault 2.0 Business Intelligence Warehousing Training Course

Course overview

Business intelligence teams often inherit warehouses built around changing reports, tightly coupled ETL pipelines and source-specific schemas. When a source system changes, historical traceability is lost, load jobs require rework and analysts wait for data engineers to remodel tables. Data Vault 2.0 provides a disciplined way to build an auditable, scalable warehouse that separates business keys, relationships, descriptive history and reporting-ready structures. This course helps participants design a warehouse that can absorb new data sources while preserving lineage and supporting trusted BI outputs.

Participants learn the Data Vault 2.0 architecture, including Hubs, Links, Satellites, Raw Vault, Business Vault and Information Marts. They practise identifying business keys, defining grain, modelling relationships, selecting hash keys, recording metadata and applying historisation rules. The course covers loading patterns, delta handling, Point-in-Time tables, Bridge tables, business-rule derivations and dimensional reporting models. Participants also learn how Data Vault structures support Power BI-style semantic models, governed metrics and repeatable analytics delivery.

Instruction combines expert-led modelling demonstrations, guided SQL and mapping exercises, peer design reviews and a running warehouse case study. Teams work from source-system extracts and business reporting requirements to construct a logical Data Vault 2.0 design, define load rules and create a reporting-layer pattern. Each participant leaves with a documented Data Vault 2.0 warehouse blueprint containing source-to-target mappings, Hub-Link-Satellite models, historisation decisions, Business Vault rules and an Information Mart design that can be adapted for workplace use.

The course is suited to data professionals who need to design, build, govern or approve modern BI warehousing solutions. It is particularly valuable for organisations replacing fragile dimensional-only warehouses, consolidating multiple operational systems or establishing a governed foundation for self-service analytics.

Course objectives

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

• Model business entities as Data Vault 2.0 Hubs using stable business keys and hash-key conventions
• Design Links and Link Satellites that capture relationships, transactions and relationship history at the correct grain
• Create Satellite structures that preserve descriptive history, record source lineage and manage change detection
• Differentiate Raw Vault, Business Vault and Information Mart responsibilities in a BI warehouse architecture
• Produce source-to-target mappings with load dates, record sources, hash-diff rules and historisation logic
• Apply incremental loading patterns for inserts, updates, late-arriving data and changed source records
• Build Point-in-Time and Bridge table designs that improve query performance for reporting workloads
• Translate Data Vault 2.0 structures into dimensional reporting marts and governed BI measures

Benefits of attending

Target competencies

Who should attend

• Data Warehouse Developers — who need to build resilient, historised warehouse structures from multiple source systems
• Data Engineers — who design ingestion and transformation pipelines for evolving operational data
• BI Developers — who need to turn Data Vault data into performant reporting models and trusted metrics
• Data Architects — who define enterprise data architecture, modelling standards and integration patterns
• Analytics Engineers — who need reproducible transformations between raw operational data and BI-ready marts
• Data Governance Leads — who require auditable lineage, source traceability and consistent historical records

Requirements and prerequisites

Participants should be comfortable reading relational data models and writing or interpreting basic SQL SELECT, JOIN and GROUP BY statements. Familiarity with facts, dimensions, primary keys, foreign keys, ETL or ELT pipelines, and a BI reporting tool is useful because the course compares Data Vault structures with dimensional marts. No prior Data Vault experience, advanced SQL, Python programming, cloud platform certification or production database administration is required. Complete beginners to data warehousing can attend, but should expect to spend additional time understanding relational modelling and warehouse terminology during the first day.

Training methodology

The course uses a single multi-source business case to connect each modelling decision to a reporting requirement. Instructor-led sessions introduce Data Vault 2.0 patterns, followed by guided whiteboard modelling, SQL-oriented load-design exercises and structured peer reviews. Participants analyse source extracts, identify keys and grain, model Hubs, Links and Satellites, then add Business Vault and Information Mart structures. Daily workshops produce reusable design artefacts, and the final session includes an application-planning review for participants’ own warehouse or integration initiative.

Course outline

Day 1: Data Vault 2.0 foundations and warehouse architecture

• Data warehouse failure modes caused by source-coupled reporting schemas
• Data Vault 2.0 principles of scalability, auditability and agility
• Raw Vault, Business Vault and Information Mart architectural layers
• Business keys, surrogate keys and hash-key selection criteria
• Hub purpose, grain and business-key uniqueness rules
• Record source, load date and technical metadata requirements
• Comparing Data Vault 2.0 with third normal form and star-schema approaches

Workshop: Participants analyse a customer-order source landscape and produce a first-pass Hub inventory with business keys, source ownership and grain statements.

Day 2: Modelling relationships and historical attributes

• Link modelling for transactions, associations and many-to-many relationships
• Link grain rules and identifying relationship business keys
• Standard Satellites for descriptive attribute historisation
• Multi-active Satellites for concurrent repeating attribute values
• Effectivity Satellites for relationship validity periods
• Hash-diff design for change detection
• Satellite splitting by rate of change, source and security classification

Workshop: Participants model Hubs, Links and Satellites for orders, customers, products and account relationships, including documented grain and hash-diff rules.

Day 3: Loading the Raw Vault and applying business rules

• Source staging patterns and persistent staging considerations
• Insert-only Raw Vault loading principles
• Incremental load logic using hash keys and hash diffs
• Handling deletes, soft deletes and source-system retractions
• Late-arriving data and out-of-sequence effective dates
• Business Vault derivations, calculated attributes and rule traceability
• Data quality controls for duplicate keys, orphan links and invalid relationships

Workshop: Participants create a source-to-target mapping and incremental-load specification for a changed customer source, including rejection, replay and data-quality rules.

Day 4: Performance structures and BI-ready information marts

• Point-in-Time tables for current-state and as-of reporting
• Bridge tables for traversing hierarchical and many-to-many relationships
• Business Vault rules versus semantic-layer calculations
• Dimensional Information Mart design from Data Vault sources
• Conformed measures, dimensions and reporting grain
• Power BI semantic model considerations for Data Vault-derived marts
• Query performance, workload isolation and refresh scheduling

Workshop: Participants design a Point-in-Time table, a Bridge table and a sales reporting mart that supports a defined set of executive Power BI measures.

Day 5: Governance, delivery planning and applied design review

• Data lineage from source record through Raw Vault to BI report
• Naming conventions and modelling documentation standards
• Metadata capture for business definitions and technical lineage
• Automated testing for keys, links, satellite changes and load completeness
• Deployment sequencing for Data Vault pipelines and reporting marts
• Data Vault 2.0 delivery roles, backlog items and design governance
• Migration planning from legacy dimensional warehouses

Workshop: Participants complete and present a Data Vault 2.0 warehouse blueprint with model diagrams, mappings, load controls, reporting mart design and a 90-day implementation plan.

Tools & standards covered

Data Vault 2.0, ANSI SQL, dbt, Microsoft Power BI

A typical training day

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