Data Mart Pros and Cons
Independent Data Marts
Independent data marts are usually the easiest and fastest to implement and their payback value can be almost immediate. Some corporations start with several data marts before deciding to build a true data warehouse. This approach has several inherent problems:
- While data marts have obvious value, they are not a true enterprise-wide solution and can become very costly over time as more and more are added.
- A major problem with proliferating data marts is that, depending on where you look for answers, there is often more than one version of the truth.
- They do not provide the historical depth of a true data warehouse.
- Because data marts are designed to handle specific types of queries from a specific type of user, they are often not good at “what if” queries like a data warehouse would be.
Logical Data Marts
Logical data marts overcome most of the limitations of independent data marts. They provide a single version of the truth. There is no historical limit to the data and “what if” querying is entirely feasible. The major drawback to logical data marts is the lack of physical control over the data. Because data in the warehouse in not pre-aggregated or dimensionalized, performance against the logical mart will not usually be as good as against an independent mart. However, use of parallelism in the logical mart can overcome some of the limitations of the non-transformed data.
Dependent Data Marts
Dependent data marts provide all advantages of a logical mart and also allow for physical control of the data as it is extracted from the data warehouse. Because dependent marts use the warehouse as their foundation, they are generally considered a better solution than independent marts, but they take longer and are more expensive to implement.