Data Vault 2.0 Modeling for Modern Data Warehousing
Learn agile, scalable, and audit-friendly data modeling with Data Vault 2.0 to power modern data warehouses in cloud and enterprise environments.
Course Duration: 10 Hours
Preview Data Vault 2.0 Modeling for Modern Data Warehousing course
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Data Vault 2.0 Modeling for Modern Data Warehousing is a specialized, self-paced course designed for data architects, data engineers, BI developers, and analytics professionals aiming to build resilient, scalable, and auditable data warehouse solutions. In an era dominated by cloud platforms, real-time data ingestion, and agile delivery expectations, traditional dimensional modeling alone is no longer sufficient. This course teaches you how to model data warehouses using Data Vault 2.0, an approach that balances flexibility, historical traceability, and enterprise-wide consistency.
The course introduces the core components of Data Vault architecture—hubs, links, satellites—and explains how they align with business keys, relationships, and contextual metadata. You’ll learn to architect a raw vault and business vault, implement loading patterns, and automate repeatable transformations using ETL/ELT tools and SQL-based workflows.
You’ll also explore the extended pillars of Data Vault 2.0, including not just modeling but agility, architecture, and methodology. The course covers metadata-driven automation, operationalization, testability, and how Data Vault adapts to modern technologies like Snowflake, BigQuery, Redshift, dbt, and DataOps platforms.
Through hands-on labs and guided design sessions, you will model warehouse schemas using real-world case studies from healthcare, finance, and retail. You will also implement version control and auditing practices to ensure traceability and regulatory compliance (e.g., GDPR, HIPAA, SOX). Whether you’re migrating from a legacy warehouse or starting a cloud-native data platform, this course gives you the patterns, templates, and methodology to succeed.
What is Data Vault 2.0?
Data Vault 2.0 is a hybrid data modeling methodology that enables enterprise data warehouses to be agile, auditable, and scalable. It separates raw historical data (Raw Vault) from business logic (Business Vault) using hubs (business keys), links (relationships), and satellites (descriptive data). Unlike dimensional or 3NF models, it supports rapid change, compliance, and automation in a cloud-first data stack.
How to Use This Course
Work through each module in order, beginning with foundational Data Vault concepts and progressing to practical modeling and automation techniques. Follow along with SQL-based examples and model diagrams. Practice by building your own Raw Vault and Business Vault structures for sample domains. Use the included templates, ETL patterns, and metadata documentation structures to accelerate implementation. Apply the techniques to your existing or planned data warehouse environments.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the principles and advantages of Data Vault 2.0 modeling.
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Design and implement Raw Vault and Business Vault structures.
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Identify and create hubs, links, and satellites from source systems.
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Automate loading patterns using SQL, dbt, or ELT pipelines.
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Apply metadata-driven design for scaling and governance.
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Build auditable and testable data pipelines.
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Integrate Data Vault with cloud data warehouses like Snowflake and BigQuery.
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Implement role-playing satellites and effectivity satellites.
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Compare Data Vault with star schema and 3NF approaches.
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Design vault solutions that support agility and regulatory compliance.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to Data Vault Modeling
- Why traditional models fall short
- History of Data Vault
- DV 2.0 vs. DV 1.0
Module 2: Data Vault 2.0 Concepts
- Hubs, Links, Satellites explained
- The Raw Vault and Business Vault
- Core principles of agility, auditability, scalability
Module 3: Hub Modeling
- Identifying business keys
- Designing hub tables
- Loading patterns for hubs
Module 4: Link Modeling
- Modeling relationships
- Link uniqueness and granularity
- Loading rules and changes
Module 5: Satellite Modeling
- Descriptive attribute tracking
- Change detection and historization
- Split satellites and multi-active rows
Module 6: Business Vault Extensions
- Derivation rules and calculations
- PIT (Point-In-Time) and Bridge tables
- Role-playing and effectivity satellites
Module 7: Automation & Metadata-Driven Design
- Generating DDL and ETL from metadata
- Using dbt and orchestration tools
- Automation case studies
Module 8: Cloud Data Vault Patterns
- Implementing in Snowflake, BigQuery, Redshift
- Data Vault in DataOps and MLOps pipelines
- Using semi-structured data (JSON, Parquet)
Module 9: Governance, Testing, and Auditing
- Lineage and traceability
- Audit columns, record source, load date
- Testing vault models for accuracy
Module 10: Project & Design Workshop
- End-to-end modeling for a business domain
- Raw Vault and Business Vault implementation
- Audit report generation
Module 11: Interview Preparation & Deployment Best Practices
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DV project lifecycle
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Agile delivery of data models
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Common anti-patterns and design trade-offs
Certification Back to Top
Upon completion of the course, learners will receive a Certificate of Completion from Uplatz, recognizing their mastery in designing and implementing modern data warehouses using the Data Vault 2.0 methodology. This certification signifies not only technical proficiency in modeling techniques but also an understanding of metadata automation, governance, and agile architecture. The certificate strengthens your professional credibility in roles requiring data platform modernization, enterprise modeling, and compliance readiness. It serves as a valuable asset for professionals pursuing roles in data engineering, data architecture, and analytics leadership.
Career & Jobs Back to Top
With increasing demand for flexible and scalable data warehousing, Data Vault 2.0 has become a crucial skill in enterprise analytics modernization. Organizations across industries are shifting from monolithic data lakes or legacy star schemas to modular and auditable vault-based models to support digital transformation, regulatory compliance, and cloud-native analytics.
By completing this course, you prepare for high-impact roles such as:
- Data Vault Modeler
- Enterprise Data Architect
- Data Warehouse Engineer
- Cloud Data Platform Consultant
- DataOps Specialist
- Analytics Architect
These roles are in high demand at Fortune 500 enterprises, cloud-native SaaS providers, financial institutions, and consulting firms. Employers such as Deloitte, Capgemini, Accenture, AWS, Snowflake, and Microsoft seek professionals who can deliver governed, scalable, and automated data warehousing solutions.
Professionals with Data Vault expertise command competitive salaries—often ₹18L to ₹40L+ in India or $110K–$180K globally. The methodology’s synergy with tools like Snowflake, dbt, and Fivetran positions certified practitioners for advanced roles in enterprise data modernization, especially as organizations adopt data mesh, DataOps, and cloud warehouse automation strategies.
Interview Questions Back to Top
1. What is Data Vault modeling and how does it differ from star schema?
Data Vault separates raw historical data from business logic using hubs, links, and satellites, unlike star schemas which integrate business logic directly.
Data Vault separates raw historical data from business logic using hubs, links, and satellites, unlike star schemas which integrate business logic directly.
2. What are hubs in Data Vault?
Hubs represent unique business keys. Each hub is a collection of identifiers with audit metadata.
Hubs represent unique business keys. Each hub is a collection of identifiers with audit metadata.
3. What is the role of a link in Data Vault?
Links represent relationships between business keys and enable many-to-many tracking in a normalized structure.
Links represent relationships between business keys and enable many-to-many tracking in a normalized structure.
4. What are satellites used for?
Satellites store descriptive attributes and historical changes associated with hubs or links.
Satellites store descriptive attributes and historical changes associated with hubs or links.
5. What is the difference between Raw Vault and Business Vault?
Raw Vault stores untransformed, auditable data; Business Vault adds derived data and business rules.
Raw Vault stores untransformed, auditable data; Business Vault adds derived data and business rules.
6. What are PIT tables in Data Vault?
Point-in-Time (PIT) tables combine satellite data for easy querying at a specific snapshot in time.
Point-in-Time (PIT) tables combine satellite data for easy querying at a specific snapshot in time.
7. How is Data Vault 2.0 agile?
It allows incremental development, easy schema changes, and metadata-driven automation.
It allows incremental development, easy schema changes, and metadata-driven automation.
8. How does Data Vault support auditability?
By storing load timestamps, record sources, and preserving all historical data, it ensures full traceability.
By storing load timestamps, record sources, and preserving all historical data, it ensures full traceability.
9. Can Data Vault be implemented in cloud platforms?
Yes, using Snowflake, BigQuery, Redshift, and tools like dbt or Matillion for orchestration.
Yes, using Snowflake, BigQuery, Redshift, and tools like dbt or Matillion for orchestration.
10. What are common challenges in implementing Data Vault?
Over-modeling, misunderstanding granularity, lack of metadata automation, and performance tuning for satellite joins.
Over-modeling, misunderstanding granularity, lack of metadata automation, and performance tuning for satellite joins.
Course Quiz Back to Top
FAQs
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