Meetup Resources
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Case Study
Juha Korpela
Semantics in Data Architecture - Real-Life learnings
Juha Korpela explores why semantics is the missing foundation of modern data and AI, and how conceptual data modeling helps organisations create shared meaning beyond tools and platforms.
Product Based
Jonas De Keuster & Alex Higgs
Data Automation on dbt with VaultSpeed & AutomateDV
Discover how integrating Vaultspeed and AutomateDV enables end‑to‑end Data Vault automation, aligning business‑driven design with efficient, production‑ready dbt execution.
Product Based
Will Riley
Optimising Snowflake Data Storage for Speed and Efficiency
See how understanding Snowflake’s storage engine helps you design faster, cheaper queries through smarter clustering and modelling choices.
Viktor Hrtánek
Template-Driven Data Vault: A Code Centric Approach to Master Complexity
Discover how templated modelling and automated pipelines reduce complexity and accelerate Data Vault delivery.
Alex Higgs
Navigating Data Vault Success
A grounded exploration of how day‑to‑day delivery, analyst involvement, and hands‑on learning help bridge the gap between technical detail and business value, guiding teams toward Data Vault solutions that support meaningful BI results.
Jose Torres
The Strategic Edge: Choosing Data Vault for Seamless SAP Integration
Learn how Data Vault gives organisations a strategic edge in handling SAP’s complexity, enabling more flexible integration, better data quality, and faster analytical insights.
John Giles
Better Data Vault? Easier Data Vault? Actually, a lot more than Data Vault
Blending decades of experience with his town‑planning metaphor, John Giles explains how robust conceptual models and reusable patterns can transform Data Vault into a business‑friendly, scalable approach that works far beyond analytics.
Francesco Longoni
Building a Data Vault at speed
This session shows how smarter patterns, clearer alignment, and better collaboration can transform Data Vault from a technical framework into a high‑value, business‑first data platform.
Case Study
Tero-Matti Kinnen
When Everything Changes Overnight: Data's Impact on Healthcare Reform in Finland
In this real‑world story of rapid transformation, Tero‑Matti Kinnen shows how data and automation powered Southwest Finland’s overnight healthcare reform, delivering clarity, stability, and actionable insights under intense time pressure.
Neil Strange
Common Challenges with Data Vault Modelling REVISITED
Neil Strange explores the real‑world obstacles that complicate Data Vault modelling and provides actionable insights to help teams balance abstraction, design effective hubs and links, and build more reliable data platforms.
Andrew Jones
Data quality: prevention is better than the cure
With a focus on preventing problems instead of correcting them later, this session highlights how quality principles and upstream controls help teams produce cleaner data and more stable analytics.
Product Based
Alex Lai
IRiS – Simplifying Data Vault Automation
A practical look at how smarter automation, clearer patterns, and hands‑on delivery experience can simplify Data Vault development and help teams build better data platforms with confidence.
Francesco Puppini
SQL with LLMs: Chatting with Your Data
A real‑world perspective on how modelling discipline, curiosity, and practical SQL experience shape the way LLMs understand data, showing how better structures and metadata enable more natural, conversational querying
Product Based
Patrick Cuba
Data Mesh & Data Vault on Snowflake
A practitioner’s view of combining domain‑driven design, Data Vault methods, and Snowflake capabilities to shape reliable data platforms that scale with the organisation’s needs.
Roberto Zagni
Data Engineering with dbt - a pragmatic approach
A practical reflection on how lessons from the field and clear engineering principles guide a more sustainable approach to building dbt‑powered Data Vault solutions.
Product Based
Andreas Heitmann
Perfect Harmony: Modeling Data with Ellie and Haley for the Willibald Team
A concise story of practice, partnership, and iteration, showing how the right automation supports modelers in navigating real‑world challenges and producing stable, scalable Data Vault designs.
Case Study
Cristian Ivanoff
McDonalds Nordics: Enabling improved focus on modelling and the business
How McDonald’s Nordics used Data Vault and automation to replace fragmented local reporting with a unified, business‑focused data platform. The session shows how standardisation improved consistency, scalability, and time to insight across the region.
Bill Inmon
Textual Data - A Brave New World
The rise of textual data analytics expands how organisations extract meaning from unstructured sources such as documents and logs to improve decision-making.
Sreeni and Sriramn Nutulapati
Model-Driven Data Vault Construction
Applying model-driven techniques to Data Vault construction streamlines design and enhances consistency, enabling more scalable and governed enterprise data delivery.
Erik Bouvin
How Twine can efficiently move data from Data Vault to Data Mart
While Data Vault highly supports agile development, Kimball-style Data Marts usually do not.
In this session, Erik Bouvin will discuss how data can be moved between your Data Vault and Data Marts in an agile way, without data pipeline dependencies and bypassing PITs using Twine.
Twine is an efficient set-based algorithm that can be applied when you have a table in which you have recorded a history of changes and some other table with related points in time, for which you want to know which historical rows were in effect at those different time points.
Join us to learn about Erik's innovative approach.
Hung Dang
How supercharged CI/CD & Data Vault ensures data quality and development agility
Extending CI/CD practices into Data Vault environments strengthens automation, improves data quality controls and accelerates reliable analytics delivery.
Barry Devlin
Clearing Skies for Cloud Data Warehousing
Cloud-based data warehousing removes traditional infrastructure constraints, allowing organisations to scale analytics systems more flexibly and efficiently.
Neil Strange
Data Mesh and Data Vault – Never the Twain shall meet?
Contrasting Data Mesh with Data Vault highlights tensions between decentralised ownership and structured enterprise modelling in modern data architectures.
Connor Lough
Fifty First Dates with Data Vault
Repeated iterations in Data Vault projects reflect evolving understanding of modelling principles and the gradual refinement of enterprise data architecture practices.
Bruce McCartney
Agile building of Information using Data Vault 2.0
Applying agile principles to Data Vault 2.0 supports incremental delivery of structured information while maintaining governance and architectural consistency.
Product Based
Patrick Cuba
Data Vault Performance & Constraints on Snowflake
This session explores Data Vault performance on Snowflake, focusing on constraints, design choices, and optimisation strategies for scalable cloud data models.
Heli Helskyaho & Matias Helskyaho
Machine Learning in the Cloud, without any panic
This session simplifies cloud ML, focusing on practical setup, architecture choices, and moving smoothly from experimentation to production.
Doug Needham
Hear what the data is telling you! Build an enrichment platform with Data Vault
This session explores how Data Vault can power a data enrichment platform, helping you turn raw inputs into clearer, more useful signals for analysis and decision-making.
Chris Fisher
Using testing to deliver rapid business value with Data Vault
This session shows how embedding testing into Data Vault workflows helps teams deliver higher-quality data faster and with greater confidence in outcomes.
Christopher Siegfried
Why you need a Data Vault for your Data Vault
This session explains why even Data Vault implementations benefit from a governing layer, helping teams manage complexity, ensure consistency, and maintain long-term scalability.
Product Based
Petr Beles
Model Driven - Data Vault Automation with Datavault Builder
This session shows how automation can simplify Data Vault modelling and speed up delivery while maintaining structure and consistency across projects.
Wayne Eckerson
Is there a Future for Business Intelligence? Key Trends You Need to Know!
This session explores how Business Intelligence is evolving with real-time analytics, modern data stacks, and more adaptive decision-making approaches.
Juha Korpela
Capture your business needs with conceptual data modelling
Misaligned business requirements often lead to inconsistent analytics, while conceptual data modelling provides a shared structural blueprint that stabilises design decisions.
Paul Rankin
Data Mesh & Data Vault - Can they really work together?
While Data Mesh decentralises data ownership and Data Vault formalises integration layers, the discussion contrasts their strengths and how they can coexist in modern architectures.
Scott Ambler
Agile Data Warehousing/ Business Intelligence: Addressing the hard problems
Through agile delivery methods in data warehousing and BI, organisations address long-standing integration, scalability and change management challenges.
Veronika Durgin
What to do (or not do) when implementing a Data Vault - lessons from the field
Recurring implementation mistakes in Data Vault projects reveal where teams misapply modelling principles and how better delivery choices emerge.
Jacek Majchrzak
Decentralize your data using business domains (Data Mesh way)
As organisations adopt Data Mesh, data ownership is increasingly organised around business domains, changing how scalability, accountability and delivery are managed across teams.
Richard Strange
Refactoring Data Vaults with Ontologies
By introducing ontologies into Data Vault design, teams can restructure evolving models to maintain shared meaning and reduce semantic inconsistency over time.
Dominic Cahill
Agile non-invasive data governance
Rather than enforcing rigid oversight layers, agile non-invasive governance integrates control mechanisms directly into data processes to maintain compliance without slowing delivery.
Richard Adams & Paul Kinnier
Learn how to combine Data Vault automation with data governance & data quality
Combining Data Vault automation with governance and data quality raises questions about how consistency and control can be maintained at scale.
Christian Kaul
What Time Is It?
Handling time correctly in distributed data systems introduces architectural challenges that directly influence data accuracy, sequencing and operational reliability.
Neil Strange
5 most common challenges with Data Vault modelling
From modelling complexity to integration overhead, recurring Data Vault challenges reveal where architecture decisions most affect long-term maintainability.
Barry Devlin
Cutting Data Fabric and Mesh to Measure with Dr. Barry Devlin
Comparing Data Fabric with Data Mesh highlights how architectural choices influence interoperability, decentralisation and enterprise-wide data coordination.
Dirk Vermeiren
Accelerate the mapping of your business taxonomy
As data ecosystems expand, accelerating business taxonomy mapping becomes increasingly important for maintaining shared definitions and structural consistency.
Justin Mullen & Guy Adams
Why Data Vault won’t work long-term without end-to-end DataOps
Without integrated DataOps capabilities, Data Vault environments struggle to maintain operational efficiency, deployment consistency and scalable delivery over time.
Steven De Costa
Data Commons, Data Sharing and Data Marketplaces
New approaches to data sharing are changing how organisations collaborate, distribute access and create value from shared information ecosystems.
Bruce McCartney
Bringing streaming data into Data Vault in (near) real-time
As organisations demand faster insights, integrating streaming data into Data Vault architectures changes how ingestion, latency and scalability are managed.
Neil Strange
Reference Architecture for Data Vault on Snowflake with Azure
Combining Snowflake with Azure services creates a cloud-native foundation for Data Vault architectures built around scalability, orchestration and platform interoperability.
Patrick Cuba
Meet Patrick Cuba author of a new book "The Data Vault Guru"
New perspectives on Data Vault methodology highlight evolving approaches to modelling standards, implementation strategy and enterprise data architecture.
Dan Linstedt
Dan Linstedt: The Future of Data Vault
As Data Vault continues to evolve, future developments centre on scalability, automation and adapting modelling practices to increasingly complex data ecosystems.
Various
The things I wish I knew before I started my first Data Vault Project!
Early Data Vault implementations frequently expose avoidable design and delivery challenges that later inform more robust modelling and architectural decisions.
Product Based
Kent Graziano & Dmytro Yaroshenko
Why Snowflake's latest features are great for Data Vault
Recent Snowflake capabilities strengthen Data Vault implementations by improving performance, simplifying scaling and supporting more efficient cloud-based data architecture.
Drew Banin
Why the world of data analytics and Data Vault is so excited by dbt
As dbt adoption grows, its role in Data Vault environments highlights how transformation tooling improves structure, testing and scalable analytics delivery.
Case Study
Adam Smith
Data Vault User Case Study - Tokio Marine HCC
A real-world insurance implementation demonstrates how Data Vault supports scalable integration and consistent data delivery across large, regulated environments.
Case Study
Tim Scott & Jonas De Keuster
Data Vault User Case Study - Argenta Bank
In banking environments, Data Vault enables structured data integration that strengthens governance and improves consistency across reporting and analytics systems.
Case Study
Veronika Durgin
Data Vault User Case Study - Indigo AG
A real-world agricultural use case shows how Data Vault enables structured integration of complex datasets to support scalable analytics and operational insight.
Francesco Puppini & Bill Inmon
Building the Unified Star Schema
Blending dimensional modelling principles into a unified structure improves consistency between analytics layers while reducing complexity in enterprise reporting systems.
John Giles
Data Vault success? It starts with the business model!
Data Vault outcomes are shaped early by how well business concepts are structured, linking domain understanding to scalable architectural design decisions.
Neil Strange
Data Vault: What's it all about
At its core, Data Vault is a modelling methodology designed to structure enterprise data for scalability, traceability and adaptable integration across systems.
Product Based
Alan Burnett
Erwin Data Intelligence for Data Vault automation
Automation tools like erwin Data Intelligence streamline Data Vault design by reducing manual modelling effort while improving governance and structural consistency.
Alex Higgs
Jump start your data warehouse
Early-stage data warehouse design emphasises rapid yet structured setup choices that influence long-term scalability, integration and analytics performance.
Product Based
Dmytro Yaroshenko
Snowflake: A Scalable Data Platform for Data Vault
Cloud-native capabilities in Snowflake enable Data Vault implementations to scale efficiently while maintaining flexibility in modelling and enterprise data processing.
Case Study
Paul Ramsay
Why Corporate Risk Management Needs Data Vault
Data Vault strengthens risk management frameworks by providing structured, auditable data models that improve visibility and consistency across risk reporting systems.
Neil Strange
Data Vault: Business Rule Secrets
Embedding business rules within Data Vault structures clarifies how enterprise logic is applied, supporting consistent interpretation and scalable data processing.
Neil Strange
Data Vault Modelling
Structured Data Vault modelling provides a consistent framework for integrating complex enterprise data while maintaining scalability and traceability across systems.
Neil Strange
Building the Business Case
A well-structured business case connects data initiatives to measurable value, helping organisations justify investment and align stakeholders on delivery outcomes.
Neil Strange
Unlocking Data Vault
Overcoming common adoption challenges enables organisations to better realise the benefits of Data Vault through clearer design practices and improved delivery alignment.
Terry Mooney
Data Vault Automation
Automation in Data Vault accelerates delivery by standardising modelling workflows and reducing manual complexity in enterprise data architecture.
Neil Strange
Introduction to Data Vault 2.0
Data Vault 2.0 extends core modelling principles with stronger automation, agility and governance to support modern enterprise data ecosystems at scale.
Case Study
Simon Dimaline
Data Vault: Integration Architecture
Integration architecture in Data Vault defines how disparate data sources are systematically combined to support scalable and auditable enterprise data management.
Dan Linstedt
Data Vault 2.0 The Benefits
Key advantages of Data Vault 2.0 include improved automation, enhanced scalability and better alignment between governance and modern data architecture needs.
Case Study
Kent Graziano
Triple Threat Case Study: Data Vault 2.0 at Aptus Health
A real-world healthcare implementation demonstrates how Data Vault 2.0 supports scalable integration and consistent analytics across complex, regulated data ecosystems.