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In Data Warehouse Modeling, a star schema and a snowflake schema consists of Fact and Dimension tables. Fact Table: It contains all the primary keys of the dimension and associated facts or measures(is a property on which calculations can be made) like quantity sold, amount sold and average sales.
WHERE date_part('hour', TimeStamp) = 10. because the date_part () function will be evaluated for every row. You should still keep the TimeStamp in the fact table in order to aggregate over boundaries of days, like in: WHERE TimeStamp between '2010-03-22 23:30' and '2010-03-23 11:15'. which gets awkward when using dimension fields.
Location dimension is tricky, and Even Ralph Kimball recognizes that it is a challenge (see chapter 10 of Building the Data Warehouse). In you case, you actually need 5 dimensions, one for each level and its levels above (one dimension for Continent, Country, Region, City, Postal, one for Continent, Country, Region, City, etc.) When you have ...
Static dimensions are not extracted from the original data source, but are created within the context of the data warehouse. A static dimension can be loaded manually — for example with status codes — or it can be generated by a procedure, such as a date or time dimension. Also, status codes dimension is mostly a static dimension. Junk ...
A single dimension containing city. This dimension contains a country column (which is repeated) factTransactionA >- dimCity. Or you may choose to have a snowflake schema, which is. A city dimension table. a seperate country dimension table. these dimensions can be joined. factTransactionA >- dimCity >- dimCountry.
Typical values in a data quality dimension could then be “ Normal value,” “Out-of-bounds value,” “ Unlikely value,” “ Verified value,” “ Unverified value,” and “ Uncertain value.”. NB: The chosen values in a data quality dimension depends on the specific business needs in a given situation. answered Nov 29, 2011 at 12:28.
1. Yes, you need to have a primary key in your dimension table. I'm guessing the NDS is simply using the surrogate key design pattern for managing entities across disparate source systems. This isn't too uncommon... here's a good post by Thomas Kejser covering a few of the issues that arise. Bottom line, if your NDS is only tracking type-1 ...
3. Generally, all numeric quantities and measures are columns in the fact table (s). Then everything else is a dimensional attribute. Which dimension they belong in is rather pragmatic and depends on the data. In addition to the suggestions you have already received, I saw no mention of degenerate dimensions.
Dimension and Fact are key terms in OLAP database design. Fact table contains data that can be aggregate. Measures are aggregated data expressions (e. Sum of costs, Count of calls, ...) Dimension contains data that is use to generate groups and filters. Fact table without dimension data is useless. A sample: "the sum of orders is 1M" is not ...
Periodic: Rolled up summaries of transaction fact tables over a defined period of time. From these we have at least 2 options that will result in something pretty similar to a slowly changing fact table. It all depends on how your source system is set up. Option 1: Transactional based Source System.