Data Vault

Last updated Jun 6, 2024

The Data Vault methodology represents a dynamic and flexible approach to managing Big Data and evolving data connection points in your Data Warehouse. Recently, there has been a significant shift towards using Data Vaults as governed Data Lakes. This shift addresses the key challenges we’ve identified in Data Warehousing:

• Adapting to changing business environments
• Handling massive data sets
• Reducing the complexities of Data Warehouse design
• Enhancing accessibility for business users by modeling close to the business domain
• Allowing seamless integration of new data sources without affecting the existing architecture

This method is proving to be highly effective and efficient, facilitating easier design, build, population, and modification of Data Warehouses. This is where Data Warehouse Automation can be particularly beneficial.

Data Vault 2.0 is the prescriptive, industry-standard methodology for turning raw data into actionable intelligence, leading to tangible business outcomes. Follow our proactive, proven recipe and transform your raw data into information that will allow you to produce the results that your business finds most valuable.

Video about “Behind the Hype: Should you ever build a Data Vault in a Lakehouse?”
Write-optimized approach (opposed to snowflake for querying) Video Lin

• Managing numerous disparate data sources
• Accommodating frequent schema changes (DDL) in source OLTP databases

• ? Lanzing Zone (LZN)
• Raw Data Vault (RDV)
• Business Data Vault (BDV)
• Universal Data Model (UDM)

References: Dimensional Modeling