![]() ![]() ![]() the state the supplier was in at the time of the transaction no update is needed. An aggregate table summarizing facts by state continues to reflect the historical state, i.e. Transactions that reference a particular surrogate key (Supplier_Key) are then permanently bound to the time slices defined by that row of the slowly changing dimension table. ) may be used as an end date, so that the field can be included in an index, and so that null-value substitution is not required when querying. In some cases, a standardized surrogate high date (e.g. The null End_Date in row two indicates the current tuple version. Unlimited history is preserved for each insert.įor example, if the supplier relocates to Illinois the version numbers will be incremented sequentially:Īnother method is to add 'effective date' columns. This method tracks historical data by creating multiple records for a given natural key in the dimensional tables with separate surrogate keys and/or different version numbers. If one has calculated an aggregate table summarizing facts by state, it will need to be recalculated when the Supplier_State is changed. It has the advantage however that it's easy to maintain. The disadvantage of the Type 1 method is that there is no history in the data warehouse. If the supplier relocates the headquarters to Illinois the record would be overwritten: However, to optimize performance on joins use integer rather than character keys (unless the number of bytes in the character key is less than the number of bytes in the integer key). Technically, the surrogate key is not necessary, since the row will be unique by the natural key (Supplier_Code). In the above example, Supplier_Code is the natural key and Supplier_Key is a surrogate key. This methodology overwrites old with new data, and therefore does not track historical data. Higher order types are employed to guarantee the preservation of history whereas Type 0 provides the least or no control. In certain circumstances history is preserved with a Type 0. Values remain as they were at the time the dimension record was first inserted. It manages dimensional changes and no action is performed. Type 6 SCDsare also sometimes called Hybrid SCDs. For historical sales reporting purposes it may be necessary to keep a record of the fact that a particular sales person had been assigned to a particular regional office at an earlier date, whereas that sales person is now assigned to a different regional office.ĭealing with these issues involves SCD management methodologies referred to as Type 0 through 6. However, the salespeople are sometimes transferred from one regional office to another. One of these dimensions may contain data about the company's salespeople: e.g., the regional offices in which they work. This fact table would be linked to dimensions by means of foreign keys. Some scenarios can cause Referential integrity problems.įor example, a database may contain a fact table that stores sales records. Data captured by Slowly Changing Dimensions (SCDs) change slowly but unpredictably, rather than according to a regular schedule. Dimensions in data management and data warehousing contain relatively static data about such entities as geographical locations, customers, or products. ![]()
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