I have just read a very intriquing paper called ‘A Common Approach for OLTP and OLAP using an In-memory Column Database’, written by Hasso Plattner.
It’s not a(nother) revolutionairy new technical approach for Data Warehousing a Business Intelligence. It’s just a series of smaller (mostly technical and some are even quite old) innovations that together could lead to a paradigma shift  in the area of Data Warehousing.
This paper is focussing on the transactional world, because that’s where the disruption will originate. In short;
- Ever increasing multi-CPU cores
- Growth of main memory
- Column databases for transactions (!)
- Shared Nothing approach
- Solid State Disks (SSD)
- In-memory access to actual data - historic data on slower devices (or not)
- Zero-update strategies in OLTP (recognizing the imporance of history as well as the importance of parallelism)
- Not in the paper; but I see datamodels for newly build OLTP systems increasingly resembling the datamodels of the HUB in the data warehouse architecture.
Modern day Data Warehouses and Business Intelligence architectures incorporates all the above mentioned technologies/methods (well, they should!) and such an architecture therefor compensates for the weaknesses that is intrinsic for OLTP regarding OLAP.
This paper is acknowledging the above technologies/methods and uses them in a OLTP context. The amazing thing is that the OLTP system is getting a lot faster and entail a lot less system maintenance (no indices, no aggregates, materialized views or what so ever, huge compression factors, etc..).
BUT, maybe more interesting. What’s the use of a data warehouse if the OLTP world is adopting these technologies/methods? Well, the case for a data warehouse becomes thinner. At least; data warehouses as we know it; ..a materialized store of (history and actual) data loaded from various sources ...
Simply put; with the above mix of technologies and methods we are able to stop propagating data. Or in other words; we can just leave the data where it initially is created. The data warehouse will then focus on the metadata (becomes hugely important!), business rules part (although advances in this area are also big), the integration part and the fit-to-task part (make it suitable for analytics, reporting, risc management etc..). Oh....data latency is non-existent.
Data architecture - truly independent of it's task (whether it's transactional or informational). Could I live to see that?
I advise people to read the paper from Lyytnen as well :
Architectural innovations stand out as creative acts of adapting and applying latent technologies or potential to previously unarticulated user needs (Abernathy and Clark 1985). They radically deviate from an established trajectory of performance improvement, or redefine what performance means in a given industry (Chistensen and Bower 1996). They are radical (Zaltman et al. 1977) in that they significantly depart from existing alternatives and are shaped by novel, cognitive frames that need to be deployed to make sense of the innovation (Bijker 1987). Consequently, disruptive innovations are truly transformative (Abernathy and Clark 1985). To become widely adopted, disruptive architectural innovations demand provisioning of complementary assets in the form of additional innovations that make the original innovation useful over its diffusion trajectory (Abernathy and Clark 1985;Teece 1986). By doing so, disruptive innovations destroy existing competencies (Schumpeter 1934) and break down existing rules of competition.
I believe for the industry of data warehousing the above might apply. Nowadays, the new technologies and methods mentioned are increasingly used in the Data Warehouse and Business Intelligence scene. When it hits the OLTP scene it will radically change Data Warehousing and Business Intelligence as we know it.
How long will it take? Well, the latter alinea of the above quotation from lyytinen might slow things down considerably:
To become widely adopted, disruptive architectural innovations demand provisioning of complementary assets in the form of additional innovations that make the original innovation useful over its diffusion trajectory (Abernathy and Clark 1985;Teece 1986). By doing so, disruptive innovations destroy existing competencies (Schumpeter 1934) and break down existing rules of competition.
SAP, Oracle and all other vendors of OLTP applications will have some work cut out for them. But I know that these quys are working hard......just listen to the SAP folks on their last summit....
 The disruptive nature of information technology Innovations - Lyytinen, Rose, 2003, MISQ