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Establishing 'Making it Happen' as a 'Formal & Predictable' Discipline
Principles and Rules Listing Page

Add extra buffer for ETL phase

If you feel that you will take four weeks to complete ETL Design to testing, add another four weeks to it. Chances are that you will find lot of surprises.
 
This page of 'Principles and Rules' is linked to:  Data Warehousing,

Unless your data-mart is very simple, ETL is one of the most unpredictable areas in any Data Warehouse design and development work. The surprises are faced on the following areas, which are difficult to estimate in the initial phases when you are preparing a business-case:

  • The data quality of the data in the source system
  • Typically you check the data quality of some key data elements and not all the elements. When you get into designing ETL and start preliminary testing, you some across many data elements, which you took for granted, but they come out to have horrendous quality.
  • Complexity of ETL routines: When you run them on the system, they take more system load and time, than you anticipated and you have to re-work the same.
  • Change in requirements during ETL design: This is a conventional issue, and you cannot totally eliminate it.

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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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