Different Types of Fact Tables Essay Sample free essay sample

A dimension tabular array typically has two types of columns. primary keys to fact tabular arraies and textualdescreptive informations. Fact -A fact tabular array typically has two types of columns. foreign keys to dimension tabular arraies and steps those that contain numeral facts. A fact tabular array can incorporate fact’s informations on item or aggregated degree. Types of Dimensions –Slowly Changing Dimensions:Properties of a dimension that would undergo alterations over clip. It depends on the concern demand whether peculiar attribute history of alterations should be preserved in the information warehouse. This is called a Slowly Changing Attribute and a dimension incorporating such an property is called a Slowly Changing Dimension. Quickly Changing Dimensions:A dimension property that changes often is a Quickly Changing Attribute. If you don’t necessitate to track the alterations. We will write a custom essay sample on Different Types of Fact Tables Essay Sample or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page the Quickly Changing Attribute is no job. but if you do necessitate to track the alterations. utilizing a standard Slowly Changing Dimension technique can ensue in a immense rising prices of the size of the dimension. One solution is to travel the property to its ain dimension. with a separate foreign key in the fact tabular array. This new dimension is called a Quickly Changing Dimension. Junk Dimensions:A debris dimension is a individual tabular array with a combination of different and unrelated properties to avoid holding a big figure of foreign keys in the fact tabular array. Junk dimensions are frequently created to pull off the foreign keys created by Quickly Changing Dimensions. Inferred Dimensions:While lading fact records. a dimension record may non yet be ready. One solution is to bring forth an alternate key with Null for all the other properties. This should technically be called an inferred member. but is frequently called an inferred dimension. Conformed Dimensions:A Dimension that is used in multiple locations is called a conformed dimension. A conformed dimension may be used with multiple fact tabular arraies in a individual database. or across multiple informations marketplaces or informations warehouses. Debauched Dimensions:A debauched dimension is when the dimension property is stored as portion of fact tabular array. and non in a separate dimension tabular array. These are basically dimension keys for which there are no other properties. In a information warehouse. these are frequently used as the consequence of a drill through question to analyse the beginning of an aggregative figure in a study. You can utilize these values to follow back to minutess in the OLTP system. Role Playing Dimensions:A role-playing dimension is one where the same dimension key — along with its associated properties — can be joined to more than one foreign key in the fact tabular array. For illustration. a fact tabular array may include foreign keys for both Ship Date and Delivery Date. But the same day of the month dimension attributes apply to each foreign key. so you can fall in the same dimension tabular array to both foreign keys. Here the day of the month dimension is taking multiple functions to map ship day of the month every bit good as bringing day of the month. and therefore the name of Role Playing dimension. Shriveled Dimensions:A shriveled dimension is a subset of another dimension. For illustration. the Orders fact tabular array may include a foreign key for Product. but the Target fact tabular array may include a foreign key merely for ProductCategory. which is in the Product tabular array. but much less farinaceous. Making a smaller dimension tabular array. with ProductCategory as its primary key. is one manner of covering with this state of affairs of heterogenous grain. If the Product dimension is snowflaked. there is likely already a separate tabular array for ProductCategory. which can function as the Shrunken Dimension. Inactive Dimensions:Inactive dimensions are non extracted from the original informations beginning. but are created within the context of the informations warehouse. A inactive dimension can be loaded manually — for illustration with Status codifications — or it can be generated by a process. such as a Date or Time dimension. Types of Facts – Additive:Linear facts are facts that can be summed up through all of the dimensions in the fact tabular array. A gross revenues fact is a good illustration for linear fact. Semi-Additive:Semi-additive facts are facts that can be summed up for some of the dimensions in the fact tabular array. but non the others. Eg: Daily balances fact can be summed up through the clients dimension but non through the clip dimension. Non-Additive: Non-additive facts are facts that can non be summed up for any of the dimensions present in the fact tabular array. Eg: Facts which have per centums. ratios calculated.Factless Fact Table:In the existent universe. it is possible to hold a fact tabular array that contains no steps or facts. These tabular arraies are called â€Å"Factless Fact tables† . Eg: A fact tabular array which has merely merchandise key and day of the month key is a factless fact. There are no steps in this tabular array. But still you can acquire the figure merchandises sold over a period of clip. Based on the above categorizations. fact tabular arraies are categorized into two: Accumulative: This type of fact table describes what has happened over a period of clip. For illustration. this fact tabular array may depict the entire gross revenues by merchandise by shop by twenty-four hours. The facts for this type of fact tabular arraies are largely linear facts. The first illustration presented here is a cumulative fact tabular array. Snapshot: This type of fact table describes the province of things in a peculiar case of clip. and normally includes more semi-additive and non-additive facts. The 2nd illustration presented here is a snapshot fact tabular array. About these ads Types of Facts in Data WarehouseTypes of Facts in Data Warehouse A fact tabular array is the one which consists of the measurings. prosodies or facts ofbusiness procedure. These mensurable facts are used to cognize the concern value and to calculate the hereafter concern. The different types of facts are explained in item below. Linear: Linear facts are facts that can be summed up through all of the dimensions in the fact tabular array. A gross revenues fact is a good illustration for linear fact. Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact tabular array. but non the others. Eg: Daily balances fact can be summed up through the clients dimension but non through the clip dimension. Non-Additive: Non-additive facts are facts that can non be summed up for any of the dimensions present in the fact tabular array. Eg: Facts which have per centums. ratios calculated. Factless Fact Table: In the existent universe. it is possible to hold a fact tabular array that contains no steps or facts. These tabular arraies are called â€Å"Factless Fact tables† . Eg: A fact tabular array which has merely merchandise key and day of the month key is a factless fact. There are no steps in this tabular array. But still you can acquire the figure merchandises sold over a period of clip. A fact tables that contain aggregative facts are frequently called drumhead tabular arraies.