Business Intelligence: Monolithic vs. Distributed Architecture (Part III: Architectural Applications)

 

Business Intelligence Series

Now considering the 500 houses and the skyscraper model introduced in thee previous post, which do you think will be built first? A skyscraper takes 2-10 years to build, depending on the city in which is built and the architecture characteristics. A house may take 6-12 months depending on similar factors. But one needs to build 500 houses. For sure the process can be optimized when the houses look the same, though there are many constraints one needs to consider - the number of workers, tools, and the construction material available at a given time, the volume of planning, etc. 

Within a rough estimate, it can take 2-5 years for each architecture to be built considering that on the average the advantages and disadvantages from the various areas can balance each other out. Historical data are in general needed for estimating the actual development time. One can start with a rough estimate and reevaluate the estimates up and down as more information are gathered. This usually happens in Software Engineering as well. 

Monolith vs. Distributed Architecture - 500 families

There are multiple ways in which the work can be assigned to the contractors. When the houses are split between domains, each domain can have its own contractor(s) or the contractors can be specialized by knowledge areas, or a combination of the two. Contractors’ performance should be the same, though in practice no two contractors are the same. Conversely, the chances are higher for some contractors to deliver at the expected quality. It would be useful to have worked before with the contractors and have a partnership that spans years back. There are risks on both sides, even if the risks might favor one architecture over the other, and this depends also on the quality of the contractors, designs, and planning. 

The planning must be good if not perfect to assure smooth development and each day can cost money when contractors are involved. The first planning must be done for the whole project and then split individually for each contractor and/or group of buildings. A back-and-forth check between the various plans is needed. Managing by exception can work, though it can also go terribly wrong. 

Lot of communication must occur between domains to make sure that everything fits together. Especially at the beginning, all the parties must plan together, must make sure that the rules of the games (best practices, policies, procedures, processes, methodologies) are agreed upon. Oversight (governance) needs to happen at a small scale as well on aggregate to makes sure that the rules of the game are followed. 

Now, which of the architectures do you think will fit a data warehouse (DWH)? Probably multiple voices will opt for the skyscraper, at least this is how a DWH looks from the outside. However, when one evaluates the architecture behind it, it can resemble a residential complex in which parts are bound together, but there are parts that can be distributed if needed. For example, in a DWH the HR department has its own area that's isolated from the other areas as it has higher security demands. There can be 2-3 other areas that don't share objects, and they can be distributed as well. The reasons why all infrastructure is on one machine are the costs associated with the licenses, respectively the reporting tools point to only one address. 

In data marts based DWHs, there are multiple buildings within the architecture, and thus the data marts can be distributed across a wider infrastructure, with each domain responsible for its own data mart(s). The data marts are by definition domain-dependent, and this is one of the downsides imputed to this architecture. 

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