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Principles and Rules Listing Page

Handle Each Time-stamp in the Fact Table as a separate dimension

Typically there are multiple dates associated with a fact table. Place each data as a separate dimension.
 
This page of 'Principles and Rules' is linked to:  Data Warehousing,

Generally fact tables have more than one time-stamp (generally date). If it has only one time-stamp, take a hard re-look at it.

Time dimension is one of the core elements to all information requirements. Most of the analysis is linked to some time element (Monthly, quarterly, year-on-year, end of month, end of day..). Therefore most of the fact tables will have a time related keys. For example in a fact table carrying invoice level sales data, you will have Invoice date, Purchase order data, delivery date, payment received data, cheque deposit data, cheque clearing date, funds transfer date etc..

In case of single or multiple time related fields in a fact table, do the following:

  • Make each time field as a separate time dimension, linked to its own time dimension table.
  • Use surrogate key instead of actual time stamp as the foreign key in the fact and linked time dimension table. This is needed because:
    • Sometimes, the dates in the source system are manually fed and are expected dates, and they get changed with actual date, when the even happens. For example expected delivery date replaced by actual delivery date, once the delivery happens.
    • Sometime, due to a date coming from different system, there could be difference in the time and date stamps (though by few seconds)

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Relevant Links to this page
Principles & Rules → Data Warehouse application is not limited to Analytics → Principles & Rules → Store as much detailed and granular data in data warehouse as possible → Principles & Rules → Data Normalization is not the best approach in Dimensional modeling → Principles & Rules → Keep the same names and definitions for all data elements → Principles & Rules → You cannot have a super-flexible Data warehouse → Principles & Rules → Dimensional models can be extensible and scalable → Principles & Rules → Data Marts should be ideally based upon a business process and not on a department. → Principles & Rules → Business Intelligence competency groups should be well-linked with business → Practice Techniques → Aggregation Queries on slowly changing Dimensions → Practice Techniques → Documenting your data-integration system → Principles & Rules → For a Data Warehouse/Data-Mart solution, analyze well, but be decisive → Principles & Rules → Maintain a trail of the key dimensional elements from source system to loaded → Principles & Rules → Conformed dimensions are must for cross-drilling → Practice Techniques → Checksum Approach for identifying the changed records from source systems → 
 
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Relevant links to this page
Data Warehouse application is not limited to Analytics
Store as much detailed and granular data in data warehouse as possible
Data Normalization is not the best approach in Dimensional modeling
Keep the same names and definitions for all data elements
You cannot have a super-flexible Data warehouse
Dimensional models can be extensible and scalable
Data Marts should be ideally based upon a business process and not on a department.
Business Intelligence competency groups should be well-linked with business
Aggregation Queries on slowly changing Dimensions
Documenting your data-integration system
For a Data Warehouse/Data-Mart solution, analyze well, but be decisive
Maintain a trail of the key dimensional elements from source system to loaded
Conformed dimensions are must for cross-drilling
Checksum Approach for identifying the changed records from source systems
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