Merge Transformation

Its a passive transformation
Is used to concatenate fields.

Comments

Post a Comment

Popular posts from this blog

IDQ - Data Profiling

To Analyze and Understand the Data. It is done using IDQ Analyst Web Client. Helps you understand the Data Quality and configure the Trust values accordingly in Informatica MDM Data Quality and Profiling Use the data quality capabilities in the Developer tool to analyze the content and structure of your data. You can enhance the data in ways that meet your business needs. Use the Developer tool to design and run processes that achieve the following objectives: Profile data. Profiling reveals the content and structure of your data. Profiling is a key step in any data project as it can identify strengths and weaknesses in your data and help you define your project plan. Create scorecards to review data quality. A scorecard is a graphical representation of the quality measurements in a profile. Standardize data values. Standardize data to remove errors and inconsistencies that you find when you run a profile. You can standardize variations in punctuation, formatting, an...
Read more

IDQ - Consolidation Transformation

Characteristics Creates Unique/Mater Records. Is used to Dedupe the duplicate data. Sometimes these unique records are referred to as Golden records. Used almost every time after the Exact Match or Fuzzy Match Transformation. Work on a Group By Key  that you define, a key which  identifies Unique Record like customer ID Feeds as input to Human Task access byIDQ analyst tool System Ports IsSurvivor N- NonSurviving Record Y- Master Record Properties Advanced - Output Mode - All  - means Input and consolidated data will be passed to next transformation Advanced - Output Mode - Survivor Only  - means only Consolidate data will be passed to the next transformation. Strategies Simple Highest Row ID - is the default maximum minimum longest shortest most frequent most frequent non-null average Row Based Most Data -- the length of full record/row Most Filled -- Leat amount of Null/Blanks Model Exact - Most Frequent Non Blanks ...
Read more

Labeler and Standardizer Transformation

Both are Used for Data Cleansing and Standardization for Addresses, Company Names(like LTD for limited, TAS  Trading As,). Labeler Characteristics Labels Different Incoming values e.g. #$^& as Symbols or S 017242 as 99999 Properties/Strategies Label using Reference Table Label Using Character Set. Standardizer Characteristics Used for Standardizing the Data e.g. AVE or AVE or AVNUE to AVENUE Properties/Strategies Offers Removal of Spaces. Label Using Character Set. Remove Reference Table Match Remove Custome String Replace Reference Table Matches with Valid Values Replace Reference Table Matches with Custome Values Replace Custome Strings.
Read more