Data Vault 2.0: Beyond A Version Upgrade And How?

In the three decades since its conception in 1990, data vault has undergone an evolution that is nothing short of a quantum leap. From opening powerful ways to model data within an enterprise data warehouse (EDW) and enabling auditing, to storing historical data and permitting parallel loading, its capabilities have scaled multifold. The need for a futuristic data architecture – one that can turn almost any type of raw data into actionable business insights – has paved the way for Data Vault 2.0 (DV 2.0).

Breaking the schema

DV 2.0 acknowledges the changing dynamics of analytics and business insights needs, modeling requirements, and data acquisition and integration options, and provides layers of solutions to adapt to these shifts. Its multi-tenant architecture brings agility, flexibility, and scalability to data lakes and data hubs. Unlike the earlier iterations, DV 2.0 is independent of specific data environments, which results in optimized performance and impeccable resiliency to changes in source data over time. This makes DV 2.0 the ideal solution for handling large databases with massive volumes of volatile data, having a higher degree of variety and from multiple disparate sources.

DV 2.0 is real-time, cloud and NoSQL-ready, and big data-friendly. But, implementing DV 2.0 is easier said than done; it requires sound understanding of the business processes and datasets.

DV 2.0 implementation: The right way for the right reasons

DV 2.0 allows enterprises to tie structured and multi-structured data, thereby joining data across environments quickly. This aspect empowers businesses to build their EDW on multiple platforms while using the most-suited storage platform for the specific data set. In other words, DV 2.0 is real-time, cloud and NoSQL-ready, and big data-friendly. However, implementing DV 2.0 requires a good understanding of the business processes and datasets, and the value is evident within three to four sprints. Below are some of the best practices that can be considered while implementing DV 2.0:

• Design DV 2.0 considering the business objectives
• Develop a metadata-driven extract, transform, and load (ETL) code automation framework
• Record historical data changes within the tables
• Apply appropriate level of referential integrity for source data
• Test the data model with real test cases before initialization