Data Vault Basics
Here, we offer the best of breed, hybrid data modeling solution for your Enterprise Integration needs. Join the growing community today, and interact with the Data Vault community. Learn how to meet the needs of the enterprise faster, cheaper, and more reliably.
The Data Vault architecture offers a unique solution to business problems and technical problems alike. It is focused squarely at the data integration efforts across the enterprise and is built from solid foundational concepts. A key to understanding the Data Vault is understanding the business. Once the business is mapped out and the practitioner has a firm grasp on how the business operates, then the process of building the Data Vault can commence.
The Data Vault has many benefits which are produced as a by-product of the basic engineering. Sticking to the Data Vault foundational rules and standards will help get any integration project off the ground quickly and easily. There are several areas of the Data Vault which we’d like to cover with you before diving into the community / forums. In case you are interested, you can also read about some of the ISSUES faced by those who undertake the Data Vault modeling.
It is very easy to convert both 3rd normal form and Star Schema to Data Vault model architecture, here we show how to convert from 3rd normal form. Inside the community we walk through the conversion steps to go from Star Schema to Data Vault model.
A list of our recent articles can be found here:
- What to consider for naming conventions in Data Warehousing – Part 2
- Delete and change handling approaches in Data Vault without an audit trail.
- Case study Wherescape
- Delete And Change Handling Approaches In Data Vault 2.0 Without A Trail
- DATA VAULT USE CASES BEYOND CLASSICAL REPORTING: PART 2
- EFFICIENT DATA LAKE STRUCTURE
- Capturing Semi-Structured Descriptive Data
- Identifying Additional Relationships Between Documents
- Integrating Documents From Heterogeneous Sources
- Document Processing In MongoDB
- An Enterprise Document Warehouse Architecture
- Processing Enterprise Data With Documents in MongoDB
- Naming Conventions in Data Warehousing
- Maintaining Link Tables