So first he explains the two different systems, first Kimball and then Inmon. In the part about Kimball he explains that Kimball "in order to resolve differences of granularity between fact tables, conformed dimensions are used.". Hmm i never thought about that, in that manner. In my view conformed dimensions are used for getting an integrated view between fact tables and so you can use the conformed dimensions for 'hopping' between business processes and combine the measures from this different facts. So this way you can consolidate your datawarehouse. It's not quite clear what he meant by this statement. What are we trying to resolve with conformed dimensions, concerning granularity? When building an integrated view of facts with different granularities still needs some additional logic. When one fact is connected to one dimensionrecord, another fact record can connect to two dimension records. From my viewpoint, conformed dimensions are not a solution for resolving granularities between facts. They can aid building integrated views, like dashboards, etc.
Inmon explains some stuff about his metholodogy, which i agree most of it. The only point i want to make is that Datavault by Linstedt is DW2.0 proof and could be a better alternative than the relational approach of Inmon, but that's my opinion.
Okay, the bulletpoints (of Inmon):
- The long-term or short-term nature of the solution. If what you want is speed of development and good short term results, Kimball is the way to go. If you want a long-term architectural solution, then the Inmon approach is the way to go (Comment: Agree)
- If you want a tactical solution, then Kimball is the way to go. If you want a strategic solution, then Inmon is the way to go (Comment: Agree)
- The Kimball approach is very fragile. Whenever requirements undergo significant change, the Kimball solution is “brittle.” (Comment: Agree).
- In the Kimball approach, there is no single version of the truth, no system of record. In the Inmon approach, at the integrated granular level, there is a carefully crafted single version of the truth (Comment: Interresting. In the projects 've done building kimball datawarehouses we always try to build a clean ETL and what i mean by this, at first i let the garbage in the data and showed the data quality in the cubes (building a cube is easy when you have a star created already). After that i started to squeze the bad data by building error marts. So there is not really a base where the data is collected in a consolidated system. So, he is right about this).
- The Kimball approach produces results in a short amount of time. The Inmon approach produces results over a longer period of time.(Comment: Yup, Kimball is quickstarter for enterprise datawarehouses but Inmon will take the lead later in the project. on the other hand for implementing a Inmon like datawarehouse it's more likely to have more support from the higher management. With Kimball it's easier to implement the 'Think big, start small' approach)
- The Kimball architecture does not produce a foundation that can be built on over time as requirements change (Comment: agree with that)
- The Kimball architecture focuses on a narrow set of infrastructure components such as tables and indexes. The Inmon architecture is far more robust than the Kimball architecture including such things as unstructured, textual data, near line storage, archival storage and processing the tight integration of metadata into the infrastructure (Comment: Kimball approach can be built upon unstructured data, but he hasn't or doesn't want to incorporate that in his methodology. This is a less important argument)
- Choose Inmon when the information needs are not quite clear, when the information request are varied, when information requests are very ad hoc, the 'system of record' principle is important, when the datawarehouse is very strategic to a organisation or when there is time to build a consolidation layer.
- Choose Kimball when you built a datawarehouse as a silod system, when the information requests won't change that very much, when the datawarehouse is not very strategic but more tactical (departmental) or when you want to show quick results.