The 'Y' in Data Vault & DV-Driven Lakehouse: Medallion and DLT Best Practices

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Sam Bendayan presents: The 'Y' in Data Vault
Much is written and talked about regarding the nuts & bolts of Data Vault standards, but in this presentation we’re going to step back and take a look at the big picture, and then drill down into the Data Vault Standards to show why the standards exist and what they solve. Many situations arise during an implementation that are not necessarily in the book, so knowing the big picture of what we’re trying to solve with Data Vault will help you think through difficult situations in your Data Vault projects.

Luan Moreno Medeiros Maciel presents:
Vault-Driven Lakehouse: Best Practices for Data Vault with Medallion and Databricks DLT
In a modern data ecosystem, organizations demand data architectures that are scalable, governed, and business-aligned. This session showcases how Data Vault 2.1 principles—designed for agility, auditability, and enterprise scale—can be brought to life using the Medallion Architecture and Databricks LakeFlow.

Luan will walk through an end-to-end example where terabytes of data are ingested and transformed using LakeFlow pipelines, building out both the Raw Data Vault (RDV) and Business Data Vault (BDV) layers. You’ll learn how to model Hubs, Links, and Satellites using Lakehouse-native tools, integrate Point-In-Time (PIT) and Bridge tables for analytics performance, and apply data quality expectations and CI/CD best practices across the workflow.

This session provides a pragmatic blueprint for unifying Data Vault 2.1 methodology with the Databricks Data Intelligence Platform, delivering trusted, high-performance data products that scale with your business.