A conformed dimension can exist as a single dimension table that relates to multiple fact tables within the same data warehouse, or as identical dimension tables in separate data marts. Date is a common conformed dimension because its attributes day, week, month, quarter, year, etc.
A conformed product dimension with product name, description, SKU, and other common attributes could exist in multiple data marts, each containing data for one store in a chain. There may be times when you have more than one fact table in a cube, and a user may want to compare measures in the fact tables on a scorecard.
You can only do this if there is a conformed dimension between the fact tables. Keep conformed dimensions in mind when building a data warehouse. It can save you a lot of trouble down the road.
If you have a conformed dimension that is used in multiple data marts and those data marts reside on different servers, what is a best practice to keep that dimension synched? James Serra's Blog. Skip to content. Conformed dimensions Posted on November 21, by James Serra.
About James Serra James is a big data and data warehousing solution architect at Microsoft. Bookmark the permalink.
Data warehousing - What is conformed fact? What is conformed dimensions use for?
March 9, at am. Search for:. I am a big data and data warehousing solution architect at Microsoft. Proudly powered by WordPress. Weaver by WeaverTheme. Sorry, your blog cannot share posts by email.A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time.
Note: People and time sometimes are not modeled as dimensions. In a data warehousedimensions provide structured labeling information to otherwise unordered numeric measures. The dimension is a data set composed of individual, non-overlapping data elements.
The Data Warehouse Matrix, Data Marts and Conformed Dimensions
The primary functions of dimensions are threefold: to provide filtering, grouping and labelling. These functions are often described as "slice and dice". A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product.
A dimensional data element is similar to a categorical variable in statistics. Typically dimensions in a data warehouse are organized internally into one or more hierarchies. A conformed dimension is a set of data attributes that have been physically referenced in multiple database tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts.
A conformed dimension cuts across many facts. Dimensions are conformed when they are either exactly the same including keys or one is a perfect subset of the other. Most important, the row headers produced in two different answer sets from the same conformed dimension s must be able to match perfectly.
Conformed dimensions are either identical or strict mathematical subsets of the most granular, detailed dimension. Dimension tables are not conformed if the attributes are labeled differently or contain different values. Conformed dimensions come in several different flavors. At the most basic level, conformed dimensions mean exactly the same thing with every possible fact table to which they are joined. The date dimension table connected to the sales facts is identical to the date dimension connected to the inventory facts.
A junk dimension is a convenient grouping of typically low-cardinality flags and indicators. By creating an abstract dimension, these flags and indicators are removed from the fact table while placing them into a useful dimensional framework. The nature of these attributes is usually text or various flags, e.Could anyone explain to me the difference between Conformed Dimensions and Conformed Facts and how these are useful in real-time?
Conformed dimensions are dimensions which can be used across any business area - that is, the business key of the table is applicable to any subject area where the dimension has relevance, and attribute labels, definitions and values are consistent across the business. For example a date dimension with an "accounting period" attribute - "accounting period" should mean the same thing in every area of the business and hence this attribute can be used by every fact table across the business.
A conformed dimension can also be a subset of another conformed dimension - for example, the date dimension would typically be at day level, and each day may have a set of accounting period attributes.
A second conformed dimension can be formed by using just the common set of accounting period attributes and calling it an accounting period dimension. Conformed fact tables are fact tables that use conformed dimensionsand share the same characteristic of common attribute labels, definitions and values - "profit" in one area of the business means the same thing as "profit" in another area of the business.
The common attributes allow the fact tables to use the same dimension attributes. A dimension X conforms to a dimension Y if X is Y, or X is identical to Y, or attributes of X conform to Y: X and Y are at the same level of granularity, and if an attribute in X includes the PK represents an attribute in Y, the names and values are the same.
If some attributes of X are not represented in Y, then they augment Y. A conformed fact is a shared fact that is designed to be used in the same way across multiple data marts. So, the shared conformed facts mean the same thing to different star schemas. Generally dimensions having the same grain, meaning per row, with keys that can be used in more than one fact table 'conform'. For example, the geography dimension might be usable in the 'Sales', 'Staff' and 'Product' star schemas.
A way of saying let us not have each datamart run off and create a new 'Geography' dimension. Conforming a fact really amounts to standardizing the definitions of terms across individual marts.
Often, different divisions or departments use the same term in different ways. Data Management. Some name 59k Followers. Tech Sign In Page. Forgot Password? Don't have an account? Sign up. Some name 41k Followers.Role Playing Dimension - Data Warehouse Concepts
Follow Tech Sign In Page. January 04, PM. Something went wrong on our end. Please try again later. If facts exist in more than one place, then they must have the same name, units, and definition. If two facts are different, then give them different names.
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Looking for more?If you work with more than one dimensional data source, you may notice that some dimensions are structured the same, and some are not. The reason that dimensions can be structured differently is that the data sources may serve different purposes.
For example, a Customer dimension appears in a Revenue data store, but not in an Inventory data store. However, the Products dimension and the Time dimension appear in both data stores. Dimensions that appear in multiple data stores are conformed if their structure is identical for all of the following:.
For example, in two data stores for Revenue and Inventory that contain Products and Time dimensions, it is possible to define the Products and Time dimensions differently for each data store. However, for drill-through between the Products and Time dimensions to work, their structures must be identical in each data store. If you are not sure whether your dimensions are conformed, then you should check with the data modeler to ensure that the drilling through will produce meaningful results.
Ensure that each level contains a business key that has values that match your PowerCube or other DMR models. Also, you must also ensure that the Root Business Key property is set and uses the business key of the first level in the hierarchy.
This helps to ensure that you have a conformed member unique name when attempting to drill through using members from this dimension. Conformed Dimensions If you work with more than one dimensional data source, you may notice that some dimensions are structured the same, and some are not. Feedback Last updated: This second post on conformed dimensions explores different ways in which dimensions can conform.
There are several flavors of conformed dimensions. Dimensions may be identical, or may share a subset of attributes that conform. Since the shared dimensions are the same table, we know they will support drilling across.
For example, stars for proposals and orders may share the customer dimension table. This makes it possible to query orders by customer and products by customer, and then merge the results together.
But this process of drilling across does not require shared dimension tables. It works equally well if proposals and orders are in separate data marts in separate databases. As long as the stars each include dimensions that share the same structure e. We can also observe that there is compatibility between dimensions that are not identical. For example, suppose we establish budgets at the monthly level, and track spending at the daily level.
Clearly, days roll up to months. If designed correctly, it should be possible to compare data from budget and spending stars by month. The ring highlights the shared attributes; any of these can be used as the basis for comparing facts in associated fact tables. If you enjoy this blog, you can help support it by picking up a copy!
Photo by Agnes Periapselicensed under Creative Commons 2. Email This BlogThis! Newer Post Older Post Home.A conformed dimension is a dimension that has exactly the same meaning and content when being referred from different fact tables.
A conformed dimension can refer to multiple tables in multiple data marts within the same organization. For two dimension tables to be considered as conformed, they must either be identical or one must be a subset of another.
There cannot be any other type of difference between the two tables. For example, two dimension tables that are exactly the same except for the primary key are not considered conformed dimensions. Why is conformed dimension important?
This goes back to the definition of data warehouse being "integrated. The time dimension is a common conformed dimension in an organization. Usually the only rule to consider with the time dimension is whether there is a fiscal year in addition to the calendar year and the definition of a week.
Fortunately, both are relatively easy to resolve. In the case of fiscal vs. The definition of a week is also something that can be different in large organizations: Finance may use Saturday to Friday, while marketing may use Sunday to Saturday. In this case, we should decide on a definition and move on.
The nice thing about the time dimension is once these rules are set, the values in the dimension table will never change.
For example, October 16th will never become the 15th day in October. Not all conformed dimensions are as easy to produce as the time dimension. An example is the customer dimension.
In any organization with some history, there is a high likelihood that different customer databases exist in different parts of the organization. To achieve a conformed customer dimension means those data must be compared against each other, rules must be set, and data must be cleansed.
In addition, when we are doing incremental data loads into the data warehouse, we'll need to apply the same rules to the new values to make sure we are only adding truly new customers to the customer dimension. Building a conformed dimension also part of the process in master data managementor MDM. In MDM, one must not only make sure the master data dimensions are conformed, but that conformity needs to be brought back to the source systems.
Return to Data Warehouse Design.For example, consider that your goal is to drill through to product line information between two reports. The first report is based on a PowerCube package, and the second report is based on a relational package.
Each product line in the relational package should include a business key, or unique identifier. In the PowerCube model, the Source value for each category in the Product Line dimension should reference the same data value as the business key in the relational package.
When the same business keys and source values are used throughout your IBM Cognos application data, end user success with reporting and analysis will increase substantially.
Conformed dimensions are also key in successful data analysis using multiple PowerCubes. When two cubes are to be used together, as with drill through, ensure that the dimensional structure and the category source values are the same in each cube model.
Changes in the structure of a dimension in one cube, for example, by adding another level, will impact both the reports and drill-through applications that use the two cubes. Conformed dimensions allow your users to combine or cross data sources successfully when their business needs require that they do so.