What are the different types of data warehouse design?

data warehouses store a huge amount of data for use in many different fields. There are two main types of data warehouse design: from top to bottom and bottom up. Both designs have their own advantages and disadvantages. The bottom -up implementation is easier and cheaper, but is less complete and the data correlation is more sporadic. In the top -down design, the connections between data and well established are obvious, but the data may be outdated and the system is expensive to implement. Data Mart is a collection of data based on a single concept. Each data mart is a unique and complete subset of data. Each of these collections is completely correlated internally and often has a connection with external data marts. In the top down design, data marts occurs naturally when the data is inserted into the system. In the bottom -up design, the MartS data is produced directly and connected together and creates a warehouse. Although it may seem like a smaller difference, it contributes to a very different design.

Top -down method was the original design of the data warehouse. With this method, all the information contained in the system includes. Each wide entity will have its own general area in the databases. When using data, a connection between correlation data points appears and data marts appears. In addition, all data in the system remains there forever - even if the data is replaced or trivialized by later information, it will remain in the system as a record of past events.

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from below -up to the design of the data warehouse, it works from the opposite direction. The company states information as a separate data mart. Over time, additional data files are added to the system, either as their own Martr data as part of the one that already exists. When two data Marts are considered sufficiently connected, they merges into one unit.

Two data warehouse proposals have their own strong and weak points. Method from top to bottom is a huge project for even smallerdata files. Since large projects are also more expensive, it is the most expensive in terms of money and labor. If the data warehouse is completed and maintained, it is an extensive collection that contains everything the company knows.

The bottom -up process is much faster and cheaper, but because the data is specified as needed, the database will never actually be completed. In addition, correlations between data mart are only as strong as their use. If there is a strong correlation, but no users see it, it's unconnected.