How to classify dates going from Entity-Relationship to Dimensional model - Stack Overflow
The usual way to do it is for your time dimension to be at the level of a date, with columns representing the date itself, month, year, etc. The key. To create data warehouse models by using ER modeling, we first need to integrate and combine the data in various systems thematically and. PDF | On Jan 1, , Daniel L. Moody and others published From ER Models to Dimensional Models: Bridging the Gap between OLTP and.
Model usability issues[ edit ] You can help by adding to it. February In using a modeled database, users can encounter two well known issues where the returned results mean something other than the results assumed by the query author. The first is the 'fan trap'. It occurs with a master table that links to multiple tables in a one-to-many relationship.
The issue derives its name from the way the model looks when it's drawn in an entity—relationship diagram: This type of model looks similar to a star schemaa type of model used in data warehouses. When trying to calculate sums over aggregates using standard SQL over the master table, unexpected and incorrect results. The solution is to either adjust the model or the SQL.
This issue occurs mostly in databases for decision support systems, and software that queries such systems sometimes includes specific methods for handling this issue. The second issue is a 'chasm trap'. A chasm trap occurs when a model suggests the existence of a relationship between entity types, but the pathway does not exist between certain entity occurrences.
Dimensional modeling - Wikipedia
For example, a Building has one-or-more Rooms, that hold zero-or-more Computers. One would expect to be able to query the model to see all the Computers in the Building. However, Computers not currently assigned to a Room because they are under repair or somewhere else are not shown on the list. Another relation between Building and Computers is needed to capture all the computers in the building.
This last modelling issue is the result of a failure to capture all the relationships that exist in the real world in the model. See Entity-Relationship Modelling 2 for details. Entity—relationships and semantic modeling[ edit ] Semantic model[ edit ] A semantic model is a model of concepts, it is sometimes called a "platform independent model". It is an intensional model. At the latest since Carnapit is well known that: The first part comprises the embedding of a concept in the world of concepts as a whole, i.
The second part establishes the referential meaning of the concept, i. Extension model[ edit ] An extensional model is one that maps to the elements of a particular methodology or technology, and is thus a "platform specific model". Therefore, the first step in the model is to describe the business process which the model builds on.
This could for instance be a sales situation in a retail store. Declare the grain After describing the business process, the next step in the design is to declare the grain of the model. The grain of the model is the exact description of what the dimensional model should be focusing on. To clarify what the grain means, you should pick the central process and describe it with one sentence.
Furthermore, the grain sentence is what you are going to build your dimensions and fact table from. You might find it necessary to go back to this step to alter the grain due to new information gained on what your model is supposed to be able to deliver. Identify the dimensions The third step in the design process is to define the dimensions of the model.
The dimensions must be defined within the grain from the second step of the 4-step process. Dimensions are the foundation of the fact table, and is where the data for the fact table is collected. Typically dimensions are nouns like date, store, inventory etc.
These dimensions are where all the data is stored. For example, the date dimension could contain data such as year, month and weekday.
Identify the facts After defining the dimensions, the next step in the process is to make keys for the fact table. This step is to identify the numeric facts that will populate each fact table row.
This step is closely related to the business users of the system, since this is where they get access to data stored in the data warehouse. Therefore, most of the fact table rows are numerical, additive figures such as quantity or cost per unit, etc.
The neutrality of this section is disputed. Relevant discussion may be found on the talk page. Please do not remove this message until conditions to do so are met. June Learn how and when to remove this template message Dimensional normalization or snowflaking removes redundant attributes, which are known in the normal flatten de-normalized dimensions. Dimensions are strictly joined together in sub dimensions.4 Step How To Design Dimensional Modelling DataWarehouse
Snowflaking has an influence on the data structure that differs from many philosophies of data warehouses. If you are only going to do operational reports then you may be able to get by with 3NF because your operational user will be looking for very fine grain data. There are some arguments on why normalization can be useful. For example, a geographic dimension may be reusable because both the customer and supplier dimensions use it.
Benefits of dimensional modeling[ edit ] This section may rely excessively on sources too closely associated with the subjectpotentially preventing the article from being verifiable and neutral. Please help improve it by replacing them with more appropriate citations to reliable, independent, third-party sources.
June Learn how and when to remove this template message Benefits of the dimensional model are the following: Compared to the normalized model, the dimensional model is easier to understand and more intuitive. In dimensional models, information is grouped into coherent business categories or dimensions, making it easier to read and interpret. Simplicity also allows software to navigate databases efficiently.
In normalized models, data is divided into many discrete entities and even a simple business process might result in dozens of tables joined together in a complex way.
Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating.