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Difference between Fact table and Dimension table?
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. Dimension Tables: Dimension tables provides descriptive information for all the measurements ...
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sql - In a datawarehouse can a dimension be related to another ...
2 I am currently developing a Data Warehouse and I was wondering if it makes sense to have a dimension connected to another via a foreign key. For example, let's say we have two dimensions 'Country' and 'City' should we store only the City Dimension Key in the fact table. And the City is aware of it's Country.
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Time and date dimension in data warehouse - Stack Overflow
19 I'm building a data warehouse. Each fact has it's timestamp. I need to create reports by day, month, quarter but by hours too. Looking at the examples I see that dates tend to be saved in dimension tables. (source: etl-tools.info) But I think, that it makes no sense for time. The dimension table would grow and grow.
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sql - Handling nulls in Datawarehouse - Stack Overflow
I'd like to ask your input on what the best practice is for handling null or empty data values when it pertains to data warehousing and SSIS/SSAS. I have several fact and dimension tables that contain null values in different rows.
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data warehouse - Database - fact table and dimension table - Stack Overflow
A dimension table is a table containing data that you don't want to constantly repeat in the fact table, for example, product data, statuses, customers etc. They are related by keys: in a star schema, each row in the fact table contains a the key of a row in the dimension table.
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Start and end period in each fact within a data warehouse
3 I have been asked to add a new table to our data warehouse. Currently, we separate our facts into monthly, quarterly, and yearly tables, with time dimensions for each. Each fact record has one time value. The data is generated in the source system by start and end period, and the end date becomes the time dimension value of the fact record.
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data warehouse - Calendar Dimension in Datawarehouse Modeling - Stack ...
1 According to the Kimball methodology, the decision to use two separate dimension tables for dates depends on the requirements and characteristics of the data. Typically, two separate date dimensions are created when there are different types of dates with distinct attributes or when there are multiple hierarchies within the date dimension.
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data warehouse - Location dimension table - Stack Overflow
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.)
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How to best handle historical data changes in a Slowly Changing ...
The Tables: The company I work for has a Slowly Changing Dimension (employee data) which has been warehoused using the Kimball Method. The dimension table containing this data has a Primary Key (...
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Data Modeling with a Time Dimension - Stack Overflow
Why not a Date Dimension ? The Date Dimension is a key dimension in data warehousing as it allows to analyze data in different aspects of date. Apart from the standard date attributes like year, quarter, month, day , hour, min ..., the date dimension can be extended to richer analysis Working with star schema model is a best practice. Date dimension is called a conformed dimension. Spliting up ...