Important Quality Transformation Summary in SAP data service – BODS – Part 5 – Match, Data Cleanse and Global Address Cleanse
2024-1-12 12:30:38 Author:查看原文) 阅读量:5 收藏

Before push the data to a data flow it is better to profile and cleanse or maintain quality of the data. For data profiling, Information Steward is the best tool which we will discuss later in a different blog.

My intention in each and every blog is to a point explanation of any object which is only required for any developer to start development. For more details, there are many places where it is described.

Today I will discuss three important Quality Transformation in SAP data service. These are given below –

1. Match:

By seeing the name you can predict that this transformation match the records and Identify the records. But Match transformation do more than that.

For an example in one of the cases It will identify that potential identical data may be duplicate from data those do not have primary key to identify the duplicated. Based on matching method an criteria it can identify those duplicated, then –

  • Classify into Master and Subordinate data
  • Fill the master data from the Subordinate data.
  • Remove the subordinate and keep only master data row.

Source Table :

There are many kind of MATCH transformation is there like Match Base, Address Match , Different type of consumer match.

By changing the configuration need to set rules for matching records then establish the thresholds and scores that BODS uses to determine near matches and matching records. it is based on the below rules-

  • Rule Based method
  • Weighted Scoring method
  • Combination method

We will use simple Base_Match for that.

Configuration in Match Wizard:

Expected Result Table : 

There are multiple scenarios where we can use Match Transformation different types.

2. Data Cleanse :

Used to clean the data to prepare for other transformation . The below option you can use to clean the data , replace a pattern of string using the below option –

  • Remove diacritical characters.
  • Information and status codes
  • Phone parser
  • Parse discrete input
  • New Data Cleanse output fields
  • New Data Cleanse input fields

For more detail you can visit the below SAP URL in which it is described in very clear and details .

3. Global_Address_Cleanse :

Global Address Cleanse transformation  are used to parse the input data ,cleanse the data , correct and standardize address data for different countries.

The address proposed field are given below –

  • Street Number
  • Street Number
  • Apartment Numbers
  • Cardinal Directions
  • Locality
  • Region
  • Zip Codes.

In the panel we need to configure the scenario and customized accordingly.

In each scenario we need to customized as per retirement.