It started out as the Common Foundational Warehouse Modeling Architecture as it’s official name. Then it was more commonly known as the “Data Vault” and became a modelling method for Data Warehouses. It also got a methodology with implementation guidelines and worked very, very well on relational platforms for many, many years.
But technology evolved. NoSQL architectures came into the picture primarily as sources. The Apache Hadoop platform started offering a cheaper storage and processing MPP architecture.
Data Vault evolved into Data Vault 2.0 and already has many successful implementations. Data Vault is now called Data Vault 1.0 (or DV 1.0) and it has a modelling focus. DV 2.0 on the other hand changes some things, and adds a LOT.
It has the following 7 differing characteristics:
1. DV 2.0 is a complete system of Business Intelligence. It talks about everything from concept to delivery. While DV 1.0 had a major focus on modelling and many of the modelling concepts are similar, DV 2.0 goes a step further and talks about data from source to business user facing constructs with guidelines for implementation, agile, virtualization and more.
2. DV 2.0 can adapt to changes better than pretty much ANY other architecture. It can do it even better than DV 1.0 because of the change in design to adapt to NoSQL and MPP platforms, if needed. DV 2.0 has successfully been implemented on MPP RDBMS platforms like Teradata as well.
3. DV 2.0 is both “big data” and “NoSQL” ready. In fact, there are implementations where data is sourced in real-time from NoSQL databases with phenomenal success stories. One of these was presented at the WWDVC 2014 where an organization saved lots of money by using this architecture.
A near real-time case study for absorbing data from MongoDB is being presented at WWDVC2015. It’s not to be missed.
4. DV 2.0 takes advantage of MPP style platforms and is designed with MPP in mind. While DV 1.0 also did this to an extent, DV 2.0 takes it to a completely other level with a zero-dependency type architecture. Of course, there are a few caveats, but we’ll cover those as well.
5. DV 2.0 lets you easily tie structured and multi-structured data where you can join data across environments easily. This particular aspect lets you build your Data Warehouse on multiple platforms while using the most suited storage platform to the particular data set. It lets you use a truly distributed Data Warehouse.
6. DV 2.0 has a greater focus on agility with principles of Disciplined Agile Delivery (DAD) embedded in the architecture. Again, being agile was certainly possible with DV 1.0, but it wasn’t a part of the methodology. DV 2.0 is not just “agile ready”, it’s completely agile.
7. DV 2.0 has a very strong focus on both automation and virtualization as much as possible. There are already a couple of automation tools in the market that have the inventors approval, and he’s open to working with more vendors.
It’s real-time ready, cloud ready, NoSQL ready and big data friendly.
And, the focus at WWDVC 2015 last year was Data Vault 2.0 with examples of sourcing it from MongoDB, with examples of virtualization, with examples of design mods, with examples of Hadoop implementations and more.
DV 2.0 is really getting ready to rock the world with it’s success stories and case studies.