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Data Warehousing and ETL Processes

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Goals of Data Warehousing



 1. “The data warehouse must make an organization’s information easily accessible.”


 2. “The data warehouse must present the organization’s information consistently.”


3. “The data warehouse must be adaptive and resilient to change.”


4. “The data warehouse must be a secure bastion that protects out information assets.”


5.“The data warehouse must serve as the foundation for improved decision     making.”


6. “The business community must accept the data warehouse if it is to be deemed successful.”


 

ETL Processes


ETL is an acronym for a three-step process to extract data from source systems and load the data to a data warehouse. The three steps of the ETL process are:



1.Extract and load Staging Tables:


 Extracts and consolidates data from one or more source systems and loads into the data warehouse staging tables.


2.Transforms the data: 


Transforms data in the staging tables and computes calculated values in preparation for the load.


3.Load Dimension and Fact Tables: 


Generates and maintains data warehouse surrogate keys and loads target dimension and fact tables.




ETL Processes





ETL processes are further refined to two types of mappings:



 1. Source Dependent Extract (SDE) mappings 


 Extracts the data from the transactional systems and loads to the data warehouse staging tables. SDE mappings are designed with respect to the source’s unique data model.

 



SDE Mapping




2. Source Independent Load (SIL) mappings 


Extracts and transforms data from the staging tables and loads to the data warehouse target tables. SIL mappings are designed to be universal with any source.



SIL Mapping





There is third type mapping known as a Post Load Processing (PLP) mapping, which is used to load aggregate tables after target tables have been loaded. This mapping is designed to be source independent.




ETL Terminology



 ETL – the process by which data is extracted from source A and transformed, aggregated and loaded into source B. ETL can be implemented by such mediums as PL/SQL or various ETL development tools.


 Full Load – the process of extracting all required data from the source and loading it into target tables. A full load will truncate all the target tables.


 Incremental Load – subsequent loads after the full load, extracting source data deltas.


Star Schema – a denormalized schema consisting of a centralized fact and one or more dimensions.


 Snowflake Schema – a normalized schema with multiple facts at different levels of grain and dimensions containing foreign key relationships to other dimensions.

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